Data processing systems for identifying, assessing, and remediating data processing risks using data modeling techniques

ABSTRACT

In various embodiments, a Data Model Adaptive Execution System may be configured to take one or more suitable actions to remediate an identified risk in view of one or more regulations (e.g., one or more legal regulations, one or more binding corporate rules, etc.). For example, in order to ensure compliance with one or more standards related to the collection and/or storage of personal data, an entity may be required to modify one or more aspects of a way in which the entity collects, stores, and/or otherwise processes personal data (e.g., in response to a change in a legal or other requirement). In order to identify whether a particular change or other risk trigger requires remediation, the system may be configured to assess a relevance of the risk posed by the risk and identify one or more processing activities or data assets that may be affected by the risk.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/159,632, filed Oct. 13, 2018, which claims priority from U.S.Provisional Patent Application Ser. No. 62/572,096, filed Oct. 13, 2017and U.S. Provisional Patent Application Ser. No. 62/728,435, filed Sep.7, 2018, and is also a continuation-in-part of U.S. patent applicationSer. No. 15/996,208, filed Jun. 1, 2018, now U.S. Pat. No. 10,181,051,issued Jan. 15, 2019, which claims priority from U.S. Provisional PatentApplication Ser. No. 62/537,839, filed Jul. 27, 2017, and is also acontinuation-in-part of U.S. patent application Ser. No. 15/853,674,filed Dec. 22, 2017, now U.S. Pat. No. 10,019,597, issued Jul. 10, 2018,which claims priority from U.S. Provisional Patent Application Ser. No.62/541,613, filed Aug. 4, 2017, and is also a continuation-in-part ofU.S. patent application Ser. No. 15/619,455, filed Jun. 10, 2017, nowU.S. Pat. No. 9,851,966, issued Dec. 26, 2017, which is acontinuation-in-part of U.S. patent application Ser. No. 15/254,901,filed Sep. 1, 2016, now U.S. Pat. No. 9,729,583, issued Aug. 8, 2017,which claims priority from: (1) U.S. Provisional Patent Application Ser.No. 62/360,123, filed Jul. 8, 2016; (2) U.S. Provisional PatentApplication Ser. No. 62/353,802, filed Jun. 23, 2016; (3) U.S.Provisional Patent Application Ser. No. 62/348,695, filed Jun. 10, 2016.The disclosures of all of the above patent applications are herebyincorporated herein by reference in their entirety.

BACKGROUND

Over the past years, privacy and security policies, and relatedoperations have become increasingly important. Breaches in security,leading to the unauthorized access of personal data (which may includesensitive personal data) have become more frequent among companies andother organizations of all sizes. Such personal data may include, but isnot limited to, personally identifiable information (PII), which may beinformation that directly (or indirectly) identifies an individual orentity. Examples of PII include names, addresses, dates of birth, socialsecurity numbers, and biometric identifiers such as a person'sfingerprints or picture. Other personal data may include, for example,customers' Internet browsing habits, purchase history, or even theirpreferences (e.g., likes and dislikes, as provided or obtained throughsocial media).

Many organizations that obtain, use, and transfer personal data,including sensitive personal data, have begun to address these privacyand security issues. To manage personal data, many companies haveattempted to implement operational policies and processes that complywith legal and industry requirements. However, there is an increasingneed for improved systems and methods to manage personal data in amanner that complies with such policies.

Similarly, as individuals have become more aware of the risks associatedwith the theft or misuse of their personal data, they have soughtadditional tools to help them manage which entities process theirpersonal data. There is currently a need for improved tools that wouldallow individuals to minimize the number of entities that process theirpersonal data—especially entities that the individual doesn't activelydo business with.

SUMMARY

A computer-implemented data processing method for generating avisualization of one or more data transfers between one or more dataassets, according to various embodiments, comprises: (1) identifying oneor more data assets associated with a particular entity; (2) analyzingthe one or more data assets to identify one or more data elements storedin the identified one or more data assets; (3) defining a plurality ofphysical locations and identifying, for each of the identified one ormore data assets, a respective particular physical location of theplurality of physical locations; (4) analyzing the identified one ormore data elements to determine one or more data transfers between theone or more data systems in different particular physical locations; (5)determining one or more regulations that relate to the one or more datatransfers; and (6) generating a visual representation of the one or moredata transfers based at least in part on the one or more regulations.

A computer-implemented method of identifying and responding to one ormore potential risk triggers based on a data model, in variousembodiments, comprises: (1) identifying one or more potential risktriggers for an entity; (2) assessing and analyzing the one or morepotential risk triggers to determine a relevance of a risk posed to theentity by the one or more potential risk triggers; (3) using one or moredata modeling techniques to identify one or more data assets associatedwith the entity that may be affected by the risk; (4) determining, basedat least in part on the one or more identified data assets and therelevance of the risk, whether to take one or more actions in responseto the one or more potential risk triggers; and (5) taking a suitableaction to remediate the risk in response to identifying the one or morepotential risk triggers.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of a data model generation and population system aredescribed below. In the course of this description, reference will bemade to the accompanying drawings, which are not necessarily drawn toscale, and wherein:

FIG. 1 depicts a data model generation and population system accordingto particular embodiments.

FIG. 2 is a schematic diagram of a computer (such as the data modelgeneration server 110, or data model population server 120) that issuitable for use in various embodiments of the data model generation andpopulation system shown in FIG. 1.

FIG. 3 is a flowchart showing an example of steps performed by a DataModel Generation Module according to particular embodiments.

FIGS. 4-10 depict various exemplary visual representations of datamodels according to particular embodiments.

FIG. 11 is a flowchart showing an example of steps performed by a DataModel Population Module.

FIG. 12 is a flowchart showing an example of steps performed by a DataPopulation Questionnaire Generation Module.

FIG. 13 is a process flow for populating a data inventory according to aparticular embodiment using one or more data mapping techniques.

FIGS. 14-25 depict exemplary screen displays and graphical userinterfaces (GUIs) according to various embodiments of the system, whichmay display information associated with the system or enable access to,or interaction with, the system by one or more users (e.g., to configurea questionnaire for populating one or more inventory attributes for oneor more data models, complete one or more assessments, etc.).

FIG. 26 is a flowchart showing an example of steps performed by anIntelligent Identity Scanning Module.

FIG. 27 is schematic diagram of network architecture for an intelligentidentity scanning system 2700 according to a particular embodiment.

FIG. 28 is a schematic diagram of an asset access methodology utilizedby an intelligent identity scanning system 2700 in various embodimentsof the system.

FIG. 29 is a flowchart showing an example of processes performed by aData Subject Access Request Fulfillment Module 2900 according to variousembodiments.

FIGS. 30-31 depict exemplary screen displays and graphical userinterfaces (GUIs) according to various embodiments of the system, whichmay display information associated with the system or enable access to,or interaction with, the system by one or more users (e.g., for thepurpose of submitting a data subject access request or other suitablerequest).

FIGS. 32-35 depict exemplary screen displays and graphical userinterfaces (GUIs) according to various embodiments of the system, whichmay display information associated with the system or enable access to,or interaction with, the system by one or more users (e.g., for thepurpose of flagging one or more risks associated with one or moreparticular questionnaire questions).

FIG. 36 is a flowchart showing an example of processes performed by aCross-Border Visualization Generation Module 3600 according to variousembodiments.

FIGS. 37-38 depict exemplary screen displays and graphical userinterfaces (GUIs) according to various embodiments of the system, whichmay display information associated with the system or enable access to,or interaction with, the system by one or more users (e.g., related tocross-border visualization).

FIG. 39 is a flowchart showing an example of processes performed by anAdaptive Execution on a Data Model Module 3900 according to variousembodiments.

FIG. 40 depicts an exemplary screen display and graphical user interface(GUI) according to various embodiments of the system, which may displayinformation associated with the system or enable access to, orinteraction with, the system by one or more users.

FIG. 41 is a flowchart showing an example of processes performed by anE-mail Scanning Module 4100 according to various embodiments.

FIG. 42 depicts an exemplary screen display and graphical user interface(GUI) according to various embodiments of the system, which may displayinformation associated with the system or enable access to, orinteraction with, the system by one or more users.

FIG. 43 is a flowchart showing an example of processes performed by aWebform Crawling Module 4300 according to various embodiments.

FIG. 44 is a flowchart showing an example of processes performed by aData Asset and Webform Management Module 4400 according to yet anotherembodiment.

DETAILED DESCRIPTION

Various embodiments now will be described more fully hereinafter withreference to the accompanying drawings. It should be understood that theinvention may be embodied in many different forms and should not beconstrued as limited to the embodiments set forth herein. Rather, theseembodiments are provided so that this disclosure will be thorough andcomplete, and will fully convey the scope of the invention to thoseskilled in the art. Like numbers refer to like elements throughout.

Overview

A data model generation and population system, according to particularembodiments, is configured to generate a data model (e.g., one or moredata models) that maps one or more relationships between and/or among aplurality of data assets utilized by a corporation or other entity(e.g., individual, organization, etc.) in the context, for example, ofone or more business processes. In particular embodiments, each of theplurality of data assets (e.g., data systems) may include, for example,any entity that collects, processes, contains, and/or transfers data(e.g., such as a software application, “internet of things” computerizeddevice, database, web site, data-center, server, etc.). For example, afirst data asset may include any software or device (e.g., server orservers) utilized by a particular entity for such data collection,processing, transfer, storage, etc.

As shown in FIGS. 4 and 5, in various embodiments, the data model maystore the following information: (1) the organization that owns and/oruses a particular data asset (a primary data asset, which is shown inthe center of the data model in FIG. 4); (2) one or more departmentswithin the organization that are responsible for the data asset; (3) oneor more software applications that collect data (e.g., personal data)for storage in and/or use by the data asset (e.g., or one or more othersuitable collection assets from which the personal data that iscollected, processed, stored, etc. by the primary data asset issourced); (4) one or more particular data subjects (or categories ofdata subjects) that information is collected from for use by the dataasset; (5) one or more particular types of data that are collected byeach of the particular applications for storage in and/or use by thedata asset; (6) one or more individuals (e.g., particular individuals ortypes of individuals) that are permitted to access and/or use the datastored in, or used by, the data asset; (7) which particular types ofdata each of those individuals are allowed to access and use; and (8)one or more data assets (destination assets) that the data istransferred to for other use, and which particular data is transferredto each of those data assets. As shown in FIGS. 6 and 7, the system mayalso optionally store information regarding, for example, which businessprocesses and processing activities utilize the data asset.

In particular embodiments, the data model stores this information foreach of a plurality of different data assets and may include linksbetween, for example, a portion of the model that provides informationfor a first particular data asset and a second portion of the model thatprovides information for a second particular data asset.

In various embodiments, the data model generation and population systemmay be implemented in the context of any suitable privacy managementsystem that is configured to ensure compliance with one or more legal orindustry standards related to the collection and/or storage of privateinformation. In various embodiments, a particular organization,sub-group, or other entity may initiate a privacy campaign or otheractivity (e.g., processing activity) as part of its business activities.In such embodiments, the privacy campaign may include any undertaking bya particular organization (e.g., such as a project or other activity)that includes the collection, entry, and/or storage (e.g., in memory) ofany personal data associated with one or more individuals. In particularembodiments, a privacy campaign may include any project undertaken by anorganization that includes the use of personal data, or any otheractivity that could have an impact on the privacy of one or moreindividuals.

In any embodiment described herein, personal data may include, forexample: (1) the name of a particular data subject (which may be aparticular individual); (2) the data subject's address; (3) the datasubject's telephone number; (4) the data subject's e-mail address; (5)the data subject's social security number; (6) information associatedwith one or more of the data subject's credit accounts (e.g., creditcard numbers); (7) banking information for the data subject; (8)location data for the data subject (e.g., their present or pastlocation); (9) internet search history for the data subject; and/or (10)any other suitable personal information, such as other personalinformation discussed herein. In particular embodiments, such personaldata may include one or more cookies (e.g., where the individual isdirectly identifiable or may be identifiable based at least in part oninformation stored in the one or more cookies).

In particular embodiments, when generating a data model, the system may,for example: (1) identify one or more data assets associated with aparticular organization; (2) generate a data inventory for each of theone or more data assets, where the data inventory comprises informationsuch as: (a) one or more processing activities associated with each ofthe one or more data assets, (b) transfer data associated with each ofthe one or more data assets (data regarding which data is transferredto/from each of the data assets, and which data assets, or individuals,the data is received from and/or transferred to, (c) personal dataassociated with each of the one or more data assets (e.g., particulartypes of data collected, stored, processed, etc. by the one or more dataassets), and/or (d) any other suitable information; and (3) populate thedata model using one or more suitable techniques.

In particular embodiments, the one or more techniques for populating thedata model may include, for example: (1) obtaining information for thedata model by using one or more questionnaires associated with aparticular privacy campaign, processing activity, etc.; (2) using one ormore intelligent identity scanning techniques discussed herein toidentify personal data stored by the system and map such data to asuitable data model, data asset within a data model, etc.; (3) obtaininginformation for the data model from a third-party application (or otherapplication) using one or more application programming interfaces (API);and/or (4) using any other suitable technique.

In particular embodiments, the system is configured to generate andpopulate a data model substantially on the fly (e.g., as the systemreceives new data associated with particular processing activities). Instill other embodiments, the system is configured to generate andpopulate a data model based at least in part on existing informationstored by the system (e.g., in one or more data assets), for example,using one or more suitable scanning techniques described herein.

As may be understood in light of this disclosure, a particularorganization may undertake a plurality of different privacy campaigns,processing activities, etc. that involve the collection and storage ofpersonal data. In some embodiments, each of the plurality of differentprocessing activities may collect redundant data (e.g., may collect thesame personal data for a particular individual more than once), and maystore data and/or redundant data in one or more particular locations(e.g., on one or more different servers, in one or more differentdatabases, etc.). In this way, a particular organization may storepersonal data in a plurality of different locations which may includeone or more known and/or unknown locations. By generating and populatinga data model of one or more data assets that are involved in thecollection, storage and processing of such personal data, the system maybe configured to create a data model that facilitates a straightforwardretrieval of information stored by the organization as desired. Forexample, in various embodiments, the system may be configured to use adata model in substantially automatically responding to one or more dataaccess requests by an individual (e.g., or other organization). In stillother embodiments, such data model generation and population may improvethe functionality of an entity's computing systems by enabling a morestreamlined retrieval of data from the system and eliminating redundantstorage of identical data. Various embodiments of a system forgenerating and populating a data model are described more fully below.

In particular embodiments, a Cross-Border Visualization GenerationSystem is configured to: (1) identify one or more data assets associatedwith a particular entity; (2) analyze the one or more data assets toidentify one or more data elements stored in the identified one or moredata assets; (3) define a plurality of physical locations and identify,for each of the identified one or more data assets, a respectiveparticular physical location of the plurality of physical locations; (4)analyze the identified one or more data elements to determine one ormore data transfers between the one or more data systems in differentparticular physical locations; (5) determine one or more regulationsthat relate to the one or more data transfers; and (6) generate a visualrepresentation of the one or more data transfers based at least in parton the one or more regulations.

In various embodiments, a Data Model Adaptive Execution System may beconfigured to take one or more suitable actions to remediate anidentified risk trigger in view of one or more regulations (e.g., one ormore legal regulations, one or more binding corporate rules, etc.). Forexample, in order to ensure compliance with one or more legal orindustry standards related to the collection and/or storage of privateinformation (e.g., personal data), an entity may be required to modifyone or more aspects of a way in which the entity collects, stores,and/or otherwise processes personal data (e.g., in response to a changein a legal or other requirement). In order to identify whether aparticular change or other risk trigger requires remediation, the systemmay be configured to assess a relevance of the risk posed by thepotential risk trigger and identify one or more processing activities ordata assets that may be affected by the risk.

The system may, for example: (1) identify and/or detect one or morepotential risk triggers; (2) assess and analyze the potential risktriggers to determine a relevance of the risk posed by the risktriggers; (3) use data modelling techniques to identify particularprocessing activities and/or data assets that may be affected by therisk; (4) determine based on a relevance of the risk and the affectedsystems/processes whether to take one or more actions; and (5) take asuitable action in response to the risk triggers, if necessary.

The risk triggers may include, for example a change in legal or industrystandards/regulations related to the collection, storage, and/orprocessing of personal data, a data breach, or any other suitable risktrigger. The suitable actions to remediate the risk may include, forexample, generating a report and providing it to a privacy officer orother individual, automatically modifying an encryption level ofparticular data stored by the system, quarantining particular data, etc.

In various embodiments, a system may be configured to substantiallyautomatically determine whether to take one or more actions in responseto one or more identified risk triggers (e.g., data breaches, changes inregulations, etc.). For example, the system may substantiallyautomatically determine a relevance of a risk posed by (e.g., a risklevel) the one or more potential risk triggers based at least in part onone or more previously-determined responses to similar risk triggers.This may include, for example, one or more previously determinedresponses for the particular entity that has identified the current risktrigger, one or more similarly situated entities, or any other suitableentity or potential trigger.

The system may, for example: (1) compare the potential risk trigger toone or more previous risks triggers experienced by the particular entityat a previous time; (2) identify a similar previous risk trigger (e.g.,one or more previous risk triggers related to a similar change inregulation, breach of data, type of issue identified, etc.); (3)determine the relevance of the current risk trigger based at least inpart on a determined relevance of the previous risk trigger; and (4)determine whether to take one or more actions to the current risktrigger based at least in part on one or more determined actions to takein response to the previous, similar risk trigger.

Similarly, in particular embodiments, the system may be configured tosubstantially automatically determine one or more actions to take inresponse to a current potential risk trigger based on one or moreactions taken by one or more similarly situated entities to one or moreprevious, similar risk triggers. For example, the system may beconfigured to: (1) compare the potential risk trigger to one or moreprevious risk triggers experienced by one or more similarly situatedentities at a previous time; (2) identify a similar previous risktrigger (e.g., one or more previous risk triggers related to a similarchange in regulation, breach of data, and/or type of issue identified,etc. from the one or more previous risk triggers experienced by the oneor more similarly-situated entities at the previous time; (3) determinethe relevance of the current risk trigger based at least in part on adetermined relevance of the previous risk trigger (e.g., a relevancedetermined by the one or more similarly situated entities); and (4)determine one or more actions to take in response to the current risktrigger based at least in part on one or more previously determinedactions to take in response to the previous, similar risk trigger (e.g.,one or more determined actions by the one or more similarly situatedentities at the previous time).

In particular embodiments, a Data Access Webform Crawling System isconfigured to: (1) identify a webform used to collect one or more piecesof personal data; (2) robotically complete the identified webform; (3)analyze the completed webform to determine one or more processingactivities that utilize the one or more pieces of personal datacollected by the webform; (4) identify a first data asset in the datamodel that is associated with the one or more processing activities; (5)modify a data inventory for the first data asset in the data model toinclude data associated with the webform; and (6) modify the data modelto include the modified data inventory for the first data asset.

In addition, various systems and computer-implemented methods aredescribed for: (1) analyzing electronic correspondence associated with adata subject (e.g., the emails within one or more email in-boxesassociated with the data subject); (2) based on the analysis,identifying one or more entities (e.g., corporate entities) that thatthe data subject does not actively do business with (e.g., as evidencedby the fact that the data subject no longer opens emails from theentity, or has set up a rule to automatically delete emails receivedfrom the entity); (3) in response to identifying the entity as an entitythat the data subject no longer actively does business with, at leastsubstantially automatically generating a data subject access requestand, optionally, automatically submitting the data subject accessrequest to the entity.

The system may, for example, be configured to determine whether the datasubject actively does business with a particular entity (e.g., purchasesproducts from, or uses one or more services from the entity) based atleast in part on one more determined interactions of the data subjectwith one or more e-mails, or other electronic correspondence, from theentity (e.g., whether the data subject reads the one or more e-mails,selects one or more links within the e-mails, deletes the e-mailswithout reading them, has set up a rule to auto-delete emails from theentity, has set up a rule to re-route emails from the entity to aparticular folder, or other location, designated for promotionalmaterials (e.g., unwanted promotional materials), and/or has set up arule to associate emails from the entity with metadata indicating thatthe correspondence is promotional in nature or should be re-routed orauto-deleted. The system may then substantially automatically generateand/or submit a data subject access request on behalf of the datasubject that includes a request to be forgotten (e.g., a request for theentity to delete some or all of the data subject's personal data thatthe entity is processing).

Exemplary Technical Platforms

As will be appreciated by one skilled in the relevant field, the presentinvention may be, for example, embodied as a computer system, a method,or a computer program product. Accordingly, various embodiments may takethe form of an entirely hardware embodiment, an entirely softwareembodiment, or an embodiment combining software and hardware aspects.Furthermore, particular embodiments may take the form of a computerprogram product stored on a computer-readable storage medium havingcomputer-readable instructions (e.g., software) embodied in the storagemedium. Various embodiments may take the form of web-implementedcomputer software. Any suitable computer-readable storage medium may beutilized including, for example, hard disks, compact disks, DVDs,optical storage devices, and/or magnetic storage devices.

Various embodiments are described below with reference to block diagramsand flowchart illustrations of methods, apparatuses (e.g., systems), andcomputer program products. It should be understood that each block ofthe block diagrams and flowchart illustrations, and combinations ofblocks in the block diagrams and flowchart illustrations, respectively,can be implemented by a computer executing computer programinstructions. These computer program instructions may be loaded onto ageneral purpose computer, special purpose computer, or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions which execute on the computer or other programmabledata processing apparatus to create means for implementing the functionsspecified in the flowchart block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner such that the instructions stored in the computer-readable memoryproduce an article of manufacture that is configured for implementingthe function specified in the flowchart block or blocks. The computerprogram instructions may also be loaded onto a computer or otherprogrammable data processing apparatus to cause a series of operationalsteps to be performed on the computer or other programmable apparatus toproduce a computer implemented process such that the instructions thatexecute on the computer or other programmable apparatus provide stepsfor implementing the functions specified in the flowchart block orblocks.

Accordingly, blocks of the block diagrams and flowchart illustrationssupport combinations of mechanisms for performing the specifiedfunctions, combinations of steps for performing the specified functions,and program instructions for performing the specified functions. Itshould also be understood that each block of the block diagrams andflowchart illustrations, and combinations of blocks in the blockdiagrams and flowchart illustrations, can be implemented by specialpurpose hardware-based computer systems that perform the specifiedfunctions or steps, or combinations of special purpose hardware andother hardware executing appropriate computer instructions.

Example System Architecture

FIG. 1 is a block diagram of a Data Model Generation and PopulationSystem 100 according to a particular embodiment. In various embodiments,the Data Model Generation and Population System 100 is part of a privacycompliance system (also referred to as a privacy management system), orother system, which may, for example, be associated with a particularorganization and be configured to aid in compliance with one or morelegal or industry regulations related to the collection and storage ofpersonal data. In some embodiments, the Data Model Generation andPopulation System 100 is configured to: (1) generate a data model basedon one or more identified data assets, where the data model includes adata inventory associated with each of the one or more identified dataassets; (2) identify populated and unpopulated aspects of each datainventory; and (3) populate the unpopulated aspects of each datainventory using one or more techniques such as intelligent identityscanning, questionnaire response mapping, APIs, etc.

As may be understood from FIG. 1, the Data Model Generation andPopulation System 100 includes one or more computer networks 115, a DataModel Generation Server 110, a Data Model Population Server 120, anIntelligent Identity Scanning Server 130, One or More Databases 140 orother data structures, one or more remote computing devices 150 (e.g., adesktop computer, laptop computer, tablet computer, smartphone, etc.),and One or More Third Party Servers 160. In particular embodiments, theone or more computer networks 115 facilitate communication between theData Model Generation Server 110, Data Model Population Server 120,Intelligent Identity Scanning Server 130, One or More Databases 140, oneor more remote computing devices 150 (e.g., a desktop computer, laptopcomputer, tablet computer, smartphone, etc.), and One or More ThirdParty Servers 160. Although in the embodiment shown in FIG. 1, the DataModel Generation Server 110, Data Model Population Server 120,Intelligent Identity Scanning Server 130, One or More Databases 140, oneor more remote computing devices 150 (e.g., a desktop computer, laptopcomputer, tablet computer, smartphone, etc.), and One or More ThirdParty Servers 160 are shown as separate servers, it should be understoodthat in other embodiments, one or more of these servers and/or computingdevices may comprise a single server, a plurality of servers, one ormore cloud-based servers, or any other suitable configuration. It shouldbe further understood that although any particular name given to anyparticular server in the course of this description should not beunderstood to imply any limit to any functionality that such a servermay provide to the system. For example, a scanning server may beimplemented along with one or more other servers to generate, automate,execute, and/or fulfill one or more data subject access requests.Similarly, a data model population server may be configured to executeone or more scanning steps described herein, etc.

The one or more computer networks 115 may include any of a variety oftypes of wired or wireless computer networks such as the Internet, aprivate intranet, a public switch telephone network (PSTN), or any othertype of network. The communication link between The Intelligent IdentityScanning Server 130 and the One or More Third Party Servers 160 may be,for example, implemented via a Local Area Network (LAN) or via theInternet. In other embodiments, the One or More Databases 140 may bestored either fully or partially on any suitable server or combinationof servers described herein.

FIG. 2 illustrates a diagrammatic representation of a computer 200 thatcan be used within the Data Model Generation and Population System 100,for example, as a client computer (e.g., one or more remote computingdevices 130 shown in FIG. 1), or as a server computer (e.g., Data ModelGeneration Server 110 shown in FIG. 1). In particular embodiments, thecomputer 200 may be suitable for use as a computer within the context ofthe Data Model Generation and Population System 100 that is configuredto generate a data model and map one or more relationships between oneor more pieces of data that make up the model.

In particular embodiments, the computer 200 may be connected (e.g.,networked) to other computers in a LAN, an intranet, an extranet, and/orthe Internet. As noted above, the computer 200 may operate in thecapacity of a server or a client computer in a client-server networkenvironment, or as a peer computer in a peer-to-peer (or distributed)network environment. The Computer 200 may be a personal computer (PC), atablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), acellular telephone, a web appliance, a server, a network router, aswitch or bridge, or any other computer capable of executing a set ofinstructions (sequential or otherwise) that specify actions to be takenby that computer. Further, while only a single computer is illustrated,the term “computer” shall also be taken to include any collection ofcomputers that individually or jointly execute a set (or multiple sets)of instructions to perform any one or more of the methodologiesdiscussed herein.

An exemplary computer 200 includes a processing device 202, a mainmemory 204 (e.g., read-only memory (ROM), flash memory, dynamic randomaccess memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM(RDRAM), etc.), static memory 206 (e.g., flash memory, static randomaccess memory (SRAM), etc.), and a data storage device 218, whichcommunicate with each other via a bus 232.

The processing device 202 represents one or more general-purposeprocessing devices such as a microprocessor, a central processing unit,or the like. More particularly, the processing device 202 may be acomplex instruction set computing (CISC) microprocessor, reducedinstruction set computing (RISC) microprocessor, very long instructionword (VLIW) microprocessor, or processor implementing other instructionsets, or processors implementing a combination of instruction sets. Theprocessing device 202 may also be one or more special-purpose processingdevices such as an application specific integrated circuit (ASIC), afield programmable gate array (FPGA), a digital signal processor (DSP),network processor, or the like. The processing device 202 may beconfigured to execute processing logic 226 for performing variousoperations and steps discussed herein.

The computer 120 may further include a network interface device 208. Thecomputer 200 also may include a video display unit 210 (e.g., a liquidcrystal display (LCD) or a cathode ray tube (CRT)), an alphanumericinput device 212 (e.g., a keyboard), a cursor control device 214 (e.g.,a mouse), and a signal generation device 216 (e.g., a speaker).

The data storage device 218 may include a non-transitorycomputer-accessible storage medium 230 (also known as a non-transitorycomputer-readable storage medium or a non-transitory computer-readablemedium) on which is stored one or more sets of instructions (e.g.,software instructions 222) embodying any one or more of themethodologies or functions described herein. The software instructions222 may also reside, completely or at least partially, within mainmemory 204 and/or within processing device 202 during execution thereofby computer 200—main memory 204 and processing device 202 alsoconstituting computer-accessible storage media. The softwareinstructions 222 may further be transmitted or received over a network115 via network interface device 208.

While the computer-accessible storage medium 230 is shown in anexemplary embodiment to be a single medium, the term“computer-accessible storage medium” should be understood to include asingle medium or multiple media (e.g., a centralized or distributeddatabase, and/or associated caches and servers) that store the one ormore sets of instructions. The term “computer-accessible storage medium”should also be understood to include any medium that is capable ofstoring, encoding or carrying a set of instructions for execution by thecomputer and that cause the computer to perform any one or more of themethodologies of the present invention. The term “computer-accessiblestorage medium” should accordingly be understood to include, but not belimited to, solid-state memories, optical and magnetic media, etc.

Exemplary System Platform

Various embodiments of a Data Model Generation and Population System 100may be implemented in the context of any suitable system (e.g., aprivacy compliance system). For example, the Data Model Generation andPopulation System 100 may be implemented to analyze a particular companyor other organization's data assets to generate a data model for one ormore processing activities, privacy campaigns, etc. undertaken by theorganization. In particular embodiments, the system may implement one ormore modules in order to at least partially ensure compliance with oneor more regulations (e.g., legal requirements) related to the collectionand/or storage of personal data. Various aspects of the system'sfunctionality may be executed by certain system modules, including aData Model Generation Module 300, Data Model Population Module 1100,Data Population Questionnaire Generation Module 1200, IntelligentIdentity Scanning Module 2600, Data Subject Access Request FulfillmentModule 2900, Cross-Border Visualization Generation Module 3600, AdaptiveExecution on a Data Model Module 3900, E-mail Scanning Module 4100,Webform Crawling Module 4300, and Data Asset and Webform ManagementModule 4400. These modules are discussed in greater detail below.

Although these modules are presented as a series of steps, it should beunderstood in light of this disclosure that various embodiments of theData Model Generation Module 300, Data Model Population Module 1100,Data Population Questionnaire Generation Module 1200, IntelligentIdentity Scanning Module 2600, Data Subject Access Request FulfillmentModule 2900, Cross-Border Visualization Generation Module 3600, AdaptiveExecution on a Data Model Module 3900, E-mail Scanning Module 4100,Webform Crawling Module 4300, and Data Asset and Webform ManagementModule 4400 described herein may perform the steps described below in anorder other than in which they are presented. In still otherembodiments, the Data Model Generation Module 300, Data Model PopulationModule 1100, Data Population Questionnaire Generation Module 1200,Intelligent Identity Scanning Module 2600, Data Subject Access RequestFulfillment Module 2900, Cross-Border Visualization Generation Module3600, Adaptive Execution on a Data Model Module 3900, E-mail ScanningModule 4100, Webform Crawling Module 4300, and Data Asset and WebformManagement Module 4400 may omit certain steps described below. Invarious other embodiments, the Data Model Generation Module 300, DataModel Population Module 1100, Data Population Questionnaire GenerationModule 1200, Intelligent Identity Scanning Module 2600, Data SubjectAccess Request Fulfillment Module 2900, Cross-Border VisualizationGeneration Module 3600, Adaptive Execution on a Data Model Module 3900,E-mail Scanning Module 4100, Webform Crawling Module 4300, and DataAsset and Webform Management Module 4400 may perform steps in additionto those described (e.g., such as one or more steps described withrespect to one or more other modules, etc.).

In particular embodiments, the steps that the system executes whenexecuting any of the modules described herein may be performed by anysuitable computer server or combination of computer servers (e.g., anysuitable computing device, server, or combination of computing deviceand/or server described herein).

Data Model Generation Module

In particular embodiments, a Data Model Generation Module 300 isconfigured to: (1) generate a data model (e.g., a data inventory) forone or more data assets utilized by a particular organization; (2)generate a respective data inventory for each of the one or more dataassets; and (3) map one or more relationships between one or moreaspects of the data inventory, the one or more data assets, etc. withinthe data model. In particular embodiments, a data asset (e.g., datasystem, software application, etc.) may include, for example, any entitythat collects, processes, contains, and/or transfers data (e.g., such asa software application, “internet of things” computerized device,database, website, data-center, server, etc.). For example, a first dataasset may include any software or device (e.g., server or servers)utilized by a particular entity for such data collection, processing,transfer, storage, etc.

In particular embodiments, a particular data asset, or collection ofdata assets, may be utilized as part of a particular data processingactivity (e.g., direct deposit generation for payroll purposes). Invarious embodiments, a data model generation system may, on behalf of aparticular organization (e.g., entity), generate a data model thatencompasses a plurality of processing activities. In other embodiments,the system may be configured to generate a discrete data model for eachof a plurality of processing activities undertaken by an organization.

Turning to FIG. 3, in particular embodiments, when executing the DataModel Generation Module 300, the system begins, at Step 310, bygenerating a data model for one or more data assets and digitallystoring the data model in computer memory. The system may, for example,store the data model in the One or More Databases 140 described above(or any other suitable data structure). In various embodiments,generating the data model comprises generating a data structure thatcomprises information regarding one or more data assets, attributes andother elements that make up the data model. As may be understood inlight of this disclosure, the one or more data assets may include anydata assets that may be related to one another. In particularembodiments, the one or more data assets may be related by virtue ofbeing associated with a particular entity (e.g., organization). Forexample, the one or more data assets may include one or more computerservers owned, operated, or utilized by the entity that at leasttemporarily store data sent, received, or otherwise processed by theparticular entity.

In still other embodiments, the one or more data assets may comprise oneor more third party assets which may, for example, send, receive and/orprocess personal data on behalf of the particular entity. These one ormore data assets may include, for example, one or more softwareapplications (e.g., such as Expensify to collect expense information,QuickBooks to maintain and store salary information, etc.).

Continuing to step 320, the system is configured to identify a firstdata asset of the one or more data assets. In particular embodiments,the first data asset may include, for example, any entity (e.g., system)that collects, processes, contains, and/or transfers data (e.g., such asa software application, “internet of things” computerized device,database, website, data-center, server, etc.). For example, the firstdata asset may include any software or device utilized by a particularorganization for such data collection, processing, transfer, etc. Invarious embodiments, the first data asset may be associated with aparticular processing activity (e.g., the first data asset may make upat least a part of a data flow that relates to the collection, storage,transfer, access, use, etc. of a particular piece of data (e.g.,personal data)). Information regarding the first data asset may clarify,for example, one or more relationships between and/or among one or moreother data assets within a particular organization. In a particularexample, the first data asset may include a software applicationprovided by a third party (e.g., a third party vendor) with which theparticular entity interfaces for the purpose of collecting, storing, orotherwise processing personal data (e.g., personal data regardingcustomers, employees, potential customers, etc.).

In particular embodiments, the first data asset is a storage asset thatmay, for example: (1) receive one or more pieces of personal data formone or more collection assets; (2) transfer one or more pieces ofpersonal data to one or more transfer assets; and/or (3) provide accessto one or more pieces of personal data to one or more authorizedindividuals (e.g., one or more employees, managers, or other authorizedindividuals within a particular entity or organization). In a particularembodiment, the first data asset is a primary data asset associated witha particular processing activity around which the system is configuredto build a data model associated with the particular processingactivity.

In particular embodiments, the system is configured to identify thefirst data asset by scanning a plurality of computer systems associatedwith a particular entity (e.g., owned, operated, utilized, etc. by theparticular entity). In various embodiments, the system is configured toidentify the first data asset from a plurality of data assets identifiedin response to completion, by one or more users, of one or morequestionnaires.

Advancing to Step 330, the system generates a first data inventory ofthe first data asset. The data inventory may comprise, for example, oneor more inventory attributes associated with the first data asset suchas, for example: (1) one or more processing activities associated withthe first data asset; (2) transfer data associated with the first dataasset (e.g., how and where the data is being transferred to and/orfrom); (3) personal data associated with the first data asset (e.g.,what type of personal data is collected and/or stored by the first dataasset; how, and from where, the data is collected, etc.); (4) storagedata associated with the personal data (e.g., whether the data is beingstored, protected and deleted); and (5) any other suitable attributerelated to the collection, use, and transfer of personal data. In otherembodiments, the one or more inventory attributes may comprise one ormore other pieces of information such as, for example: (1) the type ofdata being stored by the first data asset; (2) an amount of data storedby the first data asset; (3) whether the data is encrypted; (4) alocation of the stored data (e.g., a physical location of one or morecomputer servers on which the data is stored); etc. In particular otherembodiments, the one or more inventory attributes may comprise one ormore pieces of information technology data related to the first dataasset (e.g., such as one or more pieces of network and/or infrastructureinformation, IP address, MAC address, etc.).

In various embodiments, the system may generate the data inventory basedat least in part on the type of first data asset. For example,particular types of data assets may have particular default inventoryattributes. In such embodiments, the system is configured to generatethe data inventory for the first data asset, which may, for example,include one or more placeholder fields to be populated by the system ata later time. In this way, the system may, for example, identifyparticular inventory attributes for a particular data asset for whichinformation and/or population of data is required as the system buildsthe data model.

As may be understood in light of this disclosure, the system may, whengenerating the data inventory for the first data asset, generate one ormore placeholder fields that may include, for example: (1) theorganization (e.g., entity) that owns and/or uses the first data asset(a primary data asset, which is shown in the center of the data model inFIG. 4); (2) one or more departments within the organization that areresponsible for the first data asset; (3) one or more softwareapplications that collect data (e.g., personal data) for storage inand/or use by the first data asset (e.g., or one or more other suitablecollection assets from which the personal data that is collected,processed, stored, etc. by the first data asset is sourced); (4) one ormore particular data subjects (or categories of data subjects) thatinformation is collected from for use by the first data asset; (5) oneor more particular types of data that are collected by each of theparticular applications for storage in and/or use by the first dataasset; (6) one or more individuals (e.g., particular individuals ortypes of individuals) that are permitted to access and/or use the datastored in, or used by, the first data asset; (7) which particular typesof data each of those individuals are allowed to access and use; and (8)one or more data assets (destination assets) that the data istransferred to from the first data asset, and which particular data istransferred to each of those data assets.

As may be understood in light of this disclosure, the system may beconfigured to generate the one or more placeholder fields based at leastin part on, for example: (1) the type of the first data asset; (2) oneor more third party vendors utilized by the particular organization; (3)a number of collection or storage assets typically associated with thetype of the first data asset; and/or (4) any other suitable factorrelated to the first data asset, its one or more inventory attributes,etc. In other embodiments, the system may substantially automaticallygenerate the one or more placeholders based at least in part on ahierarchy and/or organization of the entity for which the data model isbeing built. For example, a particular entity may have a marketingdivision, legal department, human resources department, engineeringdivision, or other suitable combination of departments that make up anoverall organization. Other particular entities may have furthersubdivisions within the organization. When generating the data inventoryfor the first data asset, the system may identify that the first dataasset will have both an associated organization and subdivision withinthe organization to which it is assigned. In this example, the systemmay be configured to store an indication in computer memory that thefirst data asset is associated with an organization and a departmentwithin the organization.

Next, at Step 340, the system modifies the data model to include thefirst data inventory and electronically links the first data inventoryto the first data asset within the data model. In various embodiments,modifying the data model may include configuring the data model to storethe data inventory in computer memory, and to digitally associate thedata inventory with the first data asset in memory.

FIGS. 4 and 5 show a data model according to a particular embodiment. Asshown in these figures, the data model may store the followinginformation for the first data asset: (1) the organization that ownsand/or uses the first data asset; (2) one or more departments within theorganization that are responsible for the first data asset; (3) one ormore applications that collect data (e.g., personal data) for storage inand/or use by the first data asset; (4) one or more particular datasubjects that information is collected from for use by the first dataasset; (5) one or more collection assets from which the first assetreceives data (e.g., personal data); (6) one or more particular types ofdata that are collected by each of the particular applications (e.g.,collection assets) for storage in and/or use by the first data asset;(7) one or more individuals (e.g., particular individuals, types ofindividuals, or other parties) that are permitted to access and/or usethe data stored in or used by the first data asset; (8) which particulartypes of data each of those individuals are allowed to access and use;and (9) one or more data assets (destination assets) the data istransferred to for other use, and which particular data is transferredto each of those data assets. As shown in FIGS. 6 and 7, the system mayalso optionally store information regarding, for example, which businessprocesses and processing activities utilize the first data asset.

As noted above, in particular embodiments, the data model stores thisinformation for each of a plurality of different data assets and mayinclude one or more links between, for example, a portion of the modelthat provides information for a first particular data asset and a secondportion of the model that provides information for a second particulardata asset.

Advancing to Step 350, the system next identifies a second data assetfrom the one or more data assets. In various embodiments, the seconddata asset may include one of the one or more inventory attributesassociated with the first data asset (e.g., the second data asset mayinclude a collection asset associated with the first data asset, adestination asset or transfer asset associated with the first dataasset, etc.). In various embodiments, as may be understood in light ofthe exemplary data models described below, a second data asset may be aprimary data asset for a second processing activity, while the firstdata asset is the primary data asset for a first processing activity. Insuch embodiments, the second data asset may be a destination asset forthe first data asset as part of the first processing activity. Thesecond data asset may then be associated with one or more seconddestination assets to which the second data asset transfers data. Inthis way, particular data assets that make up the data model may defineone or more connections that the data model is configured to map andstore in memory.

Returning to Step 360, the system is configured to identify one or moreattributes associated with the second data asset, modify the data modelto include the one or more attributes, and map the one or moreattributes of the second data asset within the data model. The systemmay, for example, generate a second data inventory for the second dataasset that comprises any suitable attribute described with respect tothe first data asset above. The system may then modify the data model toinclude the one or more attributes and store the modified data model inmemory. The system may further, in various embodiments, associate thefirst and second data assets in memory as part of the data model. Insuch embodiments, the system may be configured to electronically linkthe first data asset with the second data asset. In various embodiments,such association may indicate a relationship between the first andsecond data assets in the context of the overall data model (e.g.,because the first data asset may serve as a collection asset for thesecond data asset, etc.).

Next, at Step 370, the system may be further configured to generate avisual representation of the data model. In particular embodiments, thevisual representation of the data model comprises a data map. The visualrepresentation may, for example, include the one or more data assets,one or more connections between the one or more data assets, the one ormore inventory attributes, etc.

In particular embodiments, generating the visual representation (e.g.,visual data map) of a particular data model (e.g., data inventory) mayinclude, for example, generating a visual representation that includes:(1) a visual indication of a first data asset (e.g., a storage asset), asecond data asset (e.g., a collection asset), and a third data asset(e.g., a transfer asset); (2) a visual indication of a flow of data(e.g., personal data) from the second data asset to the first data asset(e.g., from the collection asset to the storage asset); (3) a visualindication of a flow of data (e.g., personal data) from the first dataasset to the third data asset (e.g., from the storage asset to thetransfer asset); (4) one or more visual indications of a risk levelassociated with the transfer of personal data; and/or (5) any othersuitable information related to the one or more data assets, thetransfer of data between/among the one or more data assets, access todata stored or collected by the one or more data assets, etc.

In particular embodiments, the visual indication of a particular assetmay comprise a box, symbol, shape, or other suitable visual indicator.In particular embodiments, the visual indication may comprise one ormore labels (e.g., a name of each particular data asset, a type of theasset, etc.). In still other embodiments, the visual indication of aflow of data may comprise one or more arrows. In particular embodiments,the visual representation of the data model may comprise a data flow,flowchart, or other suitable visual representation.

In various embodiments, the system is configured to display (e.g., to auser) the generated visual representation of the data model on asuitable display device.

Exemplary Data Models and Visual Representations of Data Models (e.g.,Data Maps)

FIGS. 4-10 depict exemplary data models according to various embodimentsof the system described herein. FIG. 4, for example, depicts anexemplary data model that does not include a particular processingactivity (e.g., that is not associated with a particular processingactivity). As may be understood from the data model shown in thisfigure, a particular data asset (e.g., a primary data asset) may beassociated with a particular company (e.g., organization), ororganization within a particular company, sub-organization of aparticular organization, etc. In still other embodiments, the particularasset may be associated with one or more collection assets (e.g., one ormore data subjects from whom personal data is collected for storage bythe particular asset), one or more parties that have access to datastored by the particular asset, one or more transfer assets (e.g., oneor more assets to which data stored by the particular asset may betransferred), etc.

As may be understood from FIG. 4, a particular data model for aparticular asset may include a plurality of data elements. Whengenerating the data model for the particular asset, a system may beconfigured to substantially automatically identify one or more types ofdata elements for inclusion in the data model, and automaticallygenerate a data model that includes those identified data elements(e.g., even if one or more of those data elements must remainunpopulated because the system may not initially have access to a valuefor the particular data element). In such cases, the system may beconfigured to store a placeholder for a particular data element untilthe system is able to populate the particular data element with accuratedata.

As may be further understood from FIG. 4, the data model shown in FIG. 4may represent a portion of an overall data model. For example, in theembodiment shown in this figure, the transfer asset depicted may serveas a storage asset for another portion of the data model. In suchembodiments, the transfer asset may be associated with a respective oneor more of the types of data elements described above. In this way, thesystem may generate a data model that may build upon itself to comprisea plurality of layers as the system adds one or more new data assets,attributes, etc.

As may be further understood from FIG. 4, a particular data model mayindicate one or more parties that have access to and/or use of theprimary asset (e.g., storage asset). In such embodiments, the system maybe configured to enable the one or more parties to access one or morepieces of data (e.g., personal data) stored by the storage asset.

As shown in FIG. 4, the data model may further comprise one or morecollection assets (e.g., one or more data assets or individuals fromwhich the storage asset receives data such as personal data). In theexemplary data model (e.g., visual data map) shown in this figure, thecollection assets comprise a data subject (e.g., an individual that mayprovide data to the system for storage in the storage asset) and acollection asset (e.g., which may transfer one or more pieces of datathat the collection asset has collected to the storage asset).

FIG. 5 depicts a portion of an exemplary data model that is populatedfor the primary data asset Gusto. Gusto is a software application that,in the example shown in FIG. 5, may serve as a human resources servicethat contains financial, expense, review, time and attendance,background, and salary information for one or more employees of aparticular organization (e.g., GeneriTech). In the example of FIG. 5,the primary asset (e.g., Gusto) may be utilized by the HR (e.g., HumanResources) department of the particular organization (e.g., GeneriTech).Furthermore, the primary asset, Gusto, may collect financial informationfrom one or more data subjects (e.g., employees of the particularorganization), receive expense information transferred from Expensify(e.g., expensing software), and receive time and attendance datatransferred from Kronos (e.g., timekeeping software). In the exampleshown in FIG. 5, access to the information collected and/or stored byGusto may include, for example: (1) an ability to view and administersalary and background information by HR employees, and (2) an ability toview and administer employee review information by one or more servicemanagers. In the example shown in this figure, personal and other datacollected and stored by Gusto (e.g., salary information, etc.) may betransferred to a company banking system, to QuickBooks, and/or to an HRfile cabinet.

As may be understood from the example shown in FIG. 5, the system may beconfigured to generate a data model based around Gusto that illustratesa flow of personal data utilized by Gusto. The data model in thisexample illustrates, for example, a source of personal data collected,stored and/or processed by Gusto, a destination of such data, anindication of who has access to such data within Gusto, and anorganization and department responsible for the information collected byGusto. In particular embodiments, the data model and accompanying visualrepresentation (e.g., data map) generated by the system as described inany embodiment herein may be utilized in the context of compliance withone or more record keeping requirements related to the collection,storage, and processing of personal data.

FIGS. 6 and 7 depict an exemplary data model and related example that issimilar, in some respects, to the data model and example of FIGS. 4 and5. In the example shown in FIGS. 6 and 7, the exemplary data model andrelated example include a specific business process and processingactivity that is associated with the primary asset (Gusto). In thisexample, the business process is compensation and the specificprocessing activity is direct deposit generation in Gusto. As may beunderstood from this figure, the collection and transfer of data relatedto the storage asset of Gusto is based on a need to generate directdeposits through Gusto in order to compensate employees. Gusto generatesthe information needed to conduct a direct deposit (e.g., financial andsalary information) and then transmits this information to: (1) acompany bank system for execution of the direct deposit; (2) Quickbooksfor use in documenting the direct deposit payment; and (3) HR Filecabinet for use in documenting the salary info and other financialinformation.

As may be understood in light of this disclosure, when generating such adata model, particular pieces of data (e.g., data attributes, dataelements) may not be readily available to the system. In suchembodiment, the system is configured to identify a particular type ofdata, create a placeholder for such data in memory, and seek out (e.g.,scan for and populate) an appropriate piece of data to further populatethe data model. For example, in particular embodiments, the system mayidentify Gusto as a primary asset and recognize that Gusto storesexpense information. The system may then be configured to identify asource of the expense information (e.g., Expensify).

FIG. 8 depicts an exemplary screen display 800 that illustrates a visualrepresentation (e.g., visual data map) of a data model (e.g., a datainventory). In the example shown in FIG. 8, the data map provides avisual indication of a flow of data collected from particular datasubjects (e.g., employees 801). As may be understood from this figure,the data map illustrates that three separate data assets receive data(e.g., which may include personal data) directly from the employees 801.In this example, these three data assets include Kronos 803 (e.g., ahuman resources software application), Workday 805 (e.g., a humanresources software application), and ADP 807 (e.g., a human resourcessoftware application and payment processor). As shown in FIG. 8, thetransfer of data from the employees 801 to these assets is indicated byrespective arrows.

As further illustrated in FIG. 8, the data map indicates a transfer ofdata from Workday 805 to ADP 807 as well as to a Recovery Datacenter 809and a London HR File Center 811. As may be understood in light of thisdisclosure, the Recovery Datacenter 809 and London HR File Center 811may comprise additional data assets in the context of the data modelillustrated by the data map shown in FIG. 8. The Recover Datacenter 809may include, for example, one or more computer servers (e.g., backupservers). The London HR File Center 811 may include, for example, one ormore databases (e.g., such as the One or More Databases 140 shown inFIG. 1). AS shown in FIG. 8, each particular data asset depicted in thedata map may be shown along with a visual indication of the type of dataasset. For example, Kronos 803, Workday 805, and ADP 807 are depictedadjacent a first icon type (e.g., a computer monitor), while RecoverDatacenter 809 and London HR File Center 811 are depicted adjacent asecond and third icon type respectively (e.g., a server cluster and afile folder). In this way, the system may be configured to visuallyindicate, via the data model, particular information related to the datamodel in a relatively minimal manner.

FIG. 9 depicts an exemplary screen display 900 that illustrates a datamap of a plurality of assets 905 in tabular form (e.g., table form). Asmay be understood from this figure, a table that includes one or moreinventory attributes of each particular asset 905 in the table mayindicate, for example: (1) a managing organization 910 of eachrespective asset 905; (2) a hosting location 915 of each respectiveasset 905 (e.g., a physical storage location of each asset 905); (3) atype 920 of each respective asset 905, if known (e.g., a database,software application, server, etc.); (4) a processing activity 925associated with each respective asset 905; and/or (5) a status 930 ofeach particular data asset 905. In various embodiments, the status 930of each particular asset 905 may indicate a status of the asset 905 inthe discovery process. This may include, for example: (1) a “new” statusfor a particular asset that has recently been discovered as an assetthat processes, stores, or collects personal data on behalf of anorganization (e.g., discovered via one or more suitable techniquesdescribed herein); (2) an “in discovery” status for a particular assetfor which the system is populating or seeking to populate one or moreinventory attributes, etc.

FIG. 10 depicts an exemplary data map 1000 that includes an asset map ofa plurality of data assets 1005A-F, which may, for example, be utilizedby a particular entity in the collection, storage, and/or processing ofpersonal data. As may be understood in light of this disclosure, theplurality of data assets 1005A-F may have been discovered using anysuitable technique described herein (e.g., one or more intelligentidentity scanning techniques, one or more questionnaires, one or moreapplication programming interfaces, etc.). In various embodiments, adata inventory for each of the plurality of data assets 1005A-F maydefine, for each of the plurality of data assets 1005A-F a respectiveinventory attribute related to a storage location of the data asset.

As may be understood from this figure, the system may be configured togenerate a map that indicates a location of the plurality of data assets1005A-F for a particular entity. In the embodiment shown in this figure,locations that contain a data asset are indicated by circular indiciathat contain the number of assets present at that location. In theembodiment shown in this figure, the locations are broken down bycountry. In particular embodiments, the asset map may distinguishbetween internal assets (e.g., first party servers, etc.) andexternal/third party assets (e.g., third party owned servers or softwareapplications that the entity utilizes for data storage, transfer, etc.).

In some embodiments, the system is configured to indicate, via thevisual representation, whether one or more assets have an unknownlocation (e.g., because the data model described above may be incompletewith regard to the location). In such embodiments, the system may beconfigured to: (1) identify the asset with the unknown location; (2) useone or more data modeling techniques described herein to determine thelocation (e.g., such as pinging the asset, generating one or morequestionnaires for completion by a suitable individual, etc.); and (3)update a data model associated with the asset to include the location.

Data Model Population Module

In particular embodiments, a Data Model Population Module 1100 isconfigured to: (1) determine one or more unpopulated inventoryattributes in a data model; (2) determine one or more attribute valuesfor the one or more unpopulated inventory attributes; and (3) modify thedata model to include the one or more attribute values.

Turning to FIG. 11, in particular embodiments, when executing the DataModel Population Module 1100, the system begins, at Step 1110, byanalyzing one or more data inventories for each of the one or more dataassets in the data model. The system may, for example, identify one ormore particular data elements (e.g., inventory attributes) that make upthe one or more data inventories. The system may, in variousembodiments, scan one or more data structures associated with the datamodel to identify the one or more data inventories. In variousembodiments, the system is configured to build an inventory of existing(e.g., known) data assets and identify inventory attributes for each ofthe known data assets.

Continuing to Step 1120, the system is configured to determine, for eachof the one or more data inventories, one or more populated inventoryattributes and one or more unpopulated inventory attributes (e.g.,and/or one or more unpopulated data assets within the data model). As aparticular example related to an unpopulated data asset, when generatingand populating a data model, the system may determine that, for aparticular asset, there is a destination asset. In various embodiments,the destination asset may be known (e.g., and already stored by thesystem as part of the data model). In other embodiments, the destinationasset may be unknown (e.g., a data element that comprises thedestination asset may comprise a placeholder or other indication inmemory for the system to populate the unpopulated inventory attribute(e.g., data element).

As another particular example, a particular storage asset may beassociated with a plurality of inventory assets (e.g., stored in a datainventory associated with the storage asset). In this example, theplurality of inventory assets may include an unpopulated inventoryattribute related to a type of personal data stored in the storageasset. The system may, for example, determine that the type of personaldata is an unpopulated inventory asset for the particular storage asset.

Returning to Step 1130, the system is configured to determine, for eachof the one or more unpopulated inventory attributes, one or moreattribute values. In particular embodiments, the system may determinethe one or more attribute values using any suitable technique (e.g., anysuitable technique for populating the data model). In particularembodiments, the one or more techniques for populating the data modelmay include, for example: (1) obtaining data for the data model by usingone or more questionnaires associated with a particular privacycampaign, processing activity, etc.; (2) using one or more intelligentidentity scanning techniques discussed herein to identify personal datastored by the system and then map such data to a suitable data model;(3) using one or more application programming interfaces (API) to obtaindata for the data model from another software application; and/or (4)using any other suitable technique. Exemplary techniques for determiningthe one or more attribute values are described more fully below. Inother embodiments, the system may be configured to use such techniquesor other suitable techniques to populate one or more unpopulated dataassets within the data model.

Next, at Step 1140, the system modifies the data model to include theone or more attribute values for each of the one or more unpopulatedinventory attributes. The system may, for example, store the one or moreattributes values in computer memory, associate the one or moreattribute values with the one or more unpopulated inventory attributes,etc. In still other embodiments, the system may modify the data model toinclude the one or more data assets identified as filling one or morevacancies left within the data model by the unpopulated one or more dataassets.

Continuing to Step 1150, the system is configured to store the modifieddata model in memory. In various embodiments, the system is configuredto store the modified data model in the One or More Databases 140, or inany other suitable location. In particular embodiments, the system isconfigured to store the data model for later use by the system in theprocessing of one or more data subject access requests. In otherembodiments, the system is configured to store the data model for use inone or more privacy impact assessments performed by the system.

Data Model Population Questionnaire Generation Module

In particular embodiments, a Data Population Questionnaire GenerationModule 1200 is configured to generate a questionnaire (e.g., one or morequestionnaires) comprising one or more questions associated with one ormore particular unpopulated data attributes, and populate theunpopulated data attributes based at least in part on one or moreresponses to the questionnaire. In other embodiments, the system may beconfigured to populate the unpopulated data attributes based on one ormore responses to existing questionnaires.

In various embodiments, the one or more questionnaires may comprise oneor more processing activity questionnaires (e.g., privacy impactassessments, data privacy impact assessments, etc.) configured to elicitone or more pieces of data related to one or more undertakings by anorganization related to the collection, storage, and/or processing ofpersonal data (e.g., processing activities). In particular embodiments,the system is configured to generate the questionnaire (e.g., aquestionnaire template) based at least in part on one or more processingactivity attributes, data asset attributes (e.g., inventory attributes),or other suitable attributes discussed herein.

Turning to FIG. 12, in particular embodiments, when executing the DataPopulation Questionnaire Generation Module 1200, the system begins, atStep 1210, by identifying one or more unpopulated data attributes from adata model. The system may, for example, identify the one or moreunpopulated data attributes using any suitable technique describedabove. In particular embodiments, the one or more unpopulated dataattributes may relate to, for example, one or more processing activityor asset attributes such as: (1) one or more processing activitiesassociated with a particular data asset; (2) transfer data associatedwith the particular data asset (e.g., how and where the data storedand/or collected by the particular data asset is being transferred toand/or from); (3) personal data associated with the particular dataassets asset (e.g., what type of personal data is collected and/orstored by the particular data asset; how, and from where, the data iscollected, etc.); (4) storage data associated with the personal data(e.g., whether the data is being stored, protected and deleted); and (5)any other suitable attribute related to the collection, use, andtransfer of personal data by one or more data assets or via one or moreprocessing activities. In other embodiments, the one or more unpopulatedinventory attributes may comprise one or more other pieces ofinformation such as, for example: (1) the type of data being stored bythe particular data asset; (2) an amount of data stored by theparticular data asset; (3) whether the data is encrypted by theparticular data asset; (4) a location of the stored data (e.g., aphysical location of one or more computer servers on which the data isstored by the particular data asset); etc.

Continuing to Step 1220, the system generates a questionnaire (e.g., aquestionnaire template) comprising one or more questions associated withone or more particular unpopulated data attributes. As may be understoodin light of the above, the one or more particulate unpopulated dataattributes may relate to, for example, a particular processing activityor a particular data asset (e.g., a particular data asset utilized aspart of a particular processing activity). In various embodiments, theone or more questionnaires comprise one or more questions associatedwith the unpopulated data attribute. For example, if the data modelincludes an unpopulated data attribute related to a location of a serveron which a particular asset stores personal data, the system maygenerate a questionnaire associated with a processing activity thatutilizes the asset (e.g., or a questionnaire associated with the asset).The system may generate the questionnaire to include one or morequestions regarding the location of the server.

Returning to Step 1230, the system maps one or more responses to the oneor more questions to the associated one or more particular unpopulateddata attributes. The system may, for example, when generating thequestionnaire, associate a particular question with a particularunpopulated data attribute in computer memory. In various embodiments,the questionnaire may comprise a plurality of question/answer pairings,where the answer in the question/answer pairings maps to a particularinventory attribute for a particular data asset or processing activity.

In this way, the system may, upon receiving a response to the particularquestion, substantially automatically populate the particularunpopulated data attribute. Accordingly, at Step 1240, the systemmodifies the data model to populate the one or more responses as one ormore data elements for the one or more particular unpopulated dataattributes. In particular embodiments, the system is configured tomodify the data model such that the one or more responses are stored inassociation with the particular data element (e.g., unpopulated dataattribute) to which the system mapped it at Step 1230. In variousembodiments, the system is configured to store the modified data modelin the One or More Databases 140, or in any other suitable location. Inparticular embodiments, the system is configured to store the data modelfor later use by the system in the processing of one or more datasubject access requests. In other embodiments, the system is configuredto store the data model for use in one or more privacy impactassessments performed by the system.

Continuing to optional Step 1250, the system may be configured to modifythe questionnaire based at least in part on the one or more responses.The system may, for example, substantially dynamically add and/or removeone or more questions to/from the questionnaire based at least in parton the one or more responses (e.g., one or more response received by auser completing the questionnaire). For example, the system may, inresponse to the user providing a particular inventory attribute or newasset, generates additional questions that relate to that particularinventory attribute or asset. The system may, as the system addsadditional questions, substantially automatically map one or moreresponses to one or more other inventory attributes or assets. Forexample, in response to the user indicating that personal data for aparticular asset is stored in a particular location, the system maysubstantially automatically generate one or more additional questionsrelated to, for example, an encryption level of the storage, who hasaccess to the storage location, etc.

In still other embodiments, the system may modify the data model toinclude one or more additional assets, data attributes, inventoryattributes, etc. in response to one or more questionnaire responses. Forexample, the system may modify a data inventory for a particular assetto include a storage encryption data element (which specifies whetherthe particular asset stores particular data in an encrypted format) inresponse to receiving such data from a questionnaire. Modification of aquestionnaire is discussed more fully below with respect to FIG. 13.

Data Model Population via Questionnaire Process Flow

FIG. 13 depicts an exemplary process flow 1300 for populating a datamodel (e.g., modifying a data model to include a newly discovered dataasset, populating one or more inventory attributes for a particularprocessing activity or data asset, etc.). In particular, FIG. 13 depictsone or more exemplary data relationships between one or more particulardata attributes (e.g., processing activity attributes and/or assetattributes), a questionnaire template (e.g., a processing activitytemplate and/or a data asset template), a completed questionnaire (e.g.,a processing activity assessment and/or a data asset assessment), and adata inventory (e.g., a processing activity inventory and/or an assetinventory). As may be understood from this figure the system isconfigured to: (1) identify new data assets; (2) generate an assetinventory for identified new data assets; and (3) populate the generatedasset inventories. Systems and methods for populating the generatedinventories are described more fully below.

As may be understood from FIG. 13, a system may be configured to mapparticular processing activity attributes 1320A to each of: (1) aprocessing activity template 1330A; and (2) a processing activity datainventory 1310A. As may be understood in light of this disclosure, theprocessing activity template 1330A may comprise a plurality of questions(e.g., as part of a questionnaire), which may, for example, beconfigured to elicit discovery of one or more new data assets. Theplurality of questions may each correspond to one or more fields in theprocessing activity inventory 1310A, which may, for example, define oneor more inventory attributes of the processing activity.

In particular embodiments, the system is configured to provide aprocessing activity assessment 1340A to one or more individuals forcompletion. As may be understood from FIG. 13, the system is configuredto launch the processing activity assessment 1340A from the processingactivity inventory 1310A and further configured to create the processingactivity assessment 1340A from the processing activity template 1330.The processing activity assessment 1340A may comprise, for example, oneor more questions related to the processing activity. The system may, invarious embodiments, be configured to map one or more responses providedin the processing activity assessment 1340A to one or more correspondingfields in the processing activity inventory 1310A. The system may thenbe configured to modify the processing activity inventory 1310A toinclude the one or more responses, and store the modified inventory incomputer memory. In various embodiments, the system may be configured toapprove a processing activity assessment 1340A (e.g., receive approvalof the assessment) prior to feeding the processing activity inventoryattribute values into one or more fields and/or cells of the inventory.

As may be further understood from FIG. 13, in response to creating a newasset record (e.g., which the system may create, for example, inresponse to a new asset discovery via the processing activity assessment1340A described immediately above, or in any other suitable manner), thesystem may generate an asset inventory 1310B (e.g., a data assetinventory) that defines a plurality of inventory attributes for the newasset (e.g., new data asset).

As may be understood from FIG. 13, a system may be configured to mapparticular asset attributes 1320B to each of: (1) an asset template1330BA; and (2) an asset inventory 1310A. As may be understood in lightof this disclosure, the asset template 1330B may comprise a plurality ofquestions (e.g., as part of a questionnaire), which may, for example, beconfigured to elicit discovery of one or more processing activitiesassociated with the asset and/or one or more inventory attributes of theasset. The plurality of questions may each correspond to one or morefields in the asset inventory 1310B, which may, for example, define oneor more inventory attributes of the asset.

In particular embodiments, the system is configured to provide an assetassessment 1340B to one or more individuals for completion. As may beunderstood from FIG. 13, the system is configured to launch the assetassessment 1340B from the asset inventory 1310B and further configuredto create the asset assessment 1340B from the asset template 1330B. Theasset assessment 1340B may comprise, for example, one or more questionsrelated to the data asset. The system may, in various embodiments, beconfigured to map one or more responses provided in the asset assessment1340B to one or more corresponding fields in the asset inventory 1310B.The system may then be configured to modify the asset inventory 1310B(e.g., and/or a related processing activity inventory 1310A) to includethe one or more responses, and store the modified inventory in computermemory. In various embodiments, the system may be configured to approvean asset assessment 1340B (e.g., receive approval of the assessment)prior to feeding the asset inventory attribute values into one or morefields and/or cells of the inventory.

FIG. 13 further includes a detail view 1350 of a relationship betweenparticular data attributes 1320C with an exemplary data inventory 1310Cand a questionnaire template 1330C. As may be understood from thisdetail view 1350, a particular attribute name may map to a particularquestion title in a template 1330C as well as to a field name in anexemplary data inventory 1310C. In this way, the system may beconfigured to populate (e.g., automatically populate) a field name for aparticular inventory 1310C in response to a user providing a questiontitle as part of a questionnaire template 1330C. Similarly, a particularattribute description may map to a particular question description in atemplate 1330C as well as to a tooltip on a fieldname in an exemplarydata inventory 1310C. In this way, the system may be configured toprovide the tooltip for a particular inventory 1310C that includes thequestion description provided by a user as part of a questionnairetemplate 1330C.

As may be further understood from the detail view 1350 of FIG. 13, aparticular response type may map to a particular question type in atemplate 1330C as well as to a field type in an exemplary data inventory1310C. A particular question type may include, for example, a multiplechoice question (e.g., A, B, C, etc.), a freeform response, an integervalue, a drop down selection, etc. A particular field type may include,for example, a memo field type, a numeric field type, an integer fieldtype, a logical field type, or any other suitable field type. Aparticular data attribute may require a response type of, for example:(1) a name of an organization responsible for a data asset (e.g., a freeform response); (2) a number of days that data is stored by the dataasset (e.g., an integer value); and/or (3) any other suitable responsetype.

In still other embodiments, the system may be configured to map a one ormore attribute values to one or more answer choices in a template 1330Cas well as to one or more lists and/or responses in a data inventory1310C. The system may then be configured to populate a field in the datainventory 1310C with the one or more answer choices provided in aresponse to a question template 1330C with one or more attribute values.

Exemplary Questionnaire Generation and Completion User Experience

FIGS. 14-25 depict exemplary screen displays that a user may encounterwhen generating a questionnaire (e.g., one or more questionnaires and/ortemplates) for populating one or more data elements (e.g., inventoryattributes) of a data model for a data asset and/or processing activity.FIG. 14, for example, depicts an exemplary asset based questionnairetemplate builder 1400. As may be understood from FIG. 14, the templatebuilder may enable a user to generate an asset based questionnairetemplate that includes one or more sections 1420 related to the asset(e.g., asset information, security, disposal, processing activities,etc.). As may be understood in light of this disclosure, the system maybe configured to substantially automatically generate an asset basedquestionnaire template based at least in part on the one or moreunpopulated inventory attributes discussed above. The system may, forexample, be configured to generate a template that is configured topopulate the one or more unpopulated attributes (e.g., by elicitingresponses, via a questionnaire to one or more questions that are mappedto the attributes within the data inventory).

In various embodiments, the system is configured to enable a user tomodify a default template (e.g., or a system-created template) by, forexample, adding additional sections, adding one or more additionalquestions to a particular section, etc. In various embodiments, thesystem may provide one or more tools for modifying the template. Forexample, in the embodiment shown in FIG. 14, the system may provide auser with a draft and drop question template 1410, from which the usermay select a question type (e.g., textbox, multiple choice, etc.).

A template for an asset may include, for example: (1) one or morequestions requesting general information about the asset; (2) one ormore security-related questions about the asset; (3) one or morequestions regarding how the data asset disposes of data that it uses;and/or (4) one or more questions regarding processing activities thatinvolve the data asset. In various embodiments, each of these one ormore sections may comprise one or more specific questions that may mapto particular portions of a data model (e.g., a data map).

FIG. 15 depicts an exemplary screen display of a processing activityquestionnaire template builder 1500. The screen display shown in FIG. 15is similar to the template builder shown in FIG. 14 with respect to thedata asset based template builder. As may be understood from FIG. 15,the template builder may enable a user to generate a processing activitybased questionnaire template that includes one or more sections 1520related to the processing activity (e.g., business process information,personal data, source, storage, destinations, access and use, etc.). Asmay be understood in light of this disclosure, the system may beconfigured to substantially automatically generate a processing activitybased questionnaire template based at least in part on the one or moreunpopulated inventory attributes related to the processing activity(e.g., as discussed above). The system may, for example, be configuredto generate a template that is configured to populate the one or moreunpopulated attributes (e.g., by eliciting responses, via aquestionnaire to one or more questions that are mapped to the attributeswithin the data inventory).

In various embodiments, the system is configured to enable a user tomodify a default template (e.g., or a system-created template) by, forexample, adding additional sections, adding one or more additionalquestions to a particular section, etc. In various embodiments, thesystem may provide one or more tools for modifying the template. Forexample, in the embodiment shown in FIG. 15, the system may provide auser with a draft and drop question template 1510, from which the usermay select a question type (e.g., textbox, multiple choice, assetattributes, data subjects, etc.). The system may be further configuredto enable a user to publish a completed template (e.g., for use in aparticular assessment). In other embodiments, the system may beconfigured to substantially automatically publish the template.

In various embodiments, a template for a processing activity mayinclude, for example: (1) one or more questions related to the type ofbusiness process that involves a particular data asset; (2) one or morequestions regarding what type of personal data is acquired from datasubjects for use by a particular data asset; (3) one or more questionsrelated to a source of the acquired personal data; (4) one or morequestions related to how and/or where the personal data will be storedand/or for how long; (5) one or more questions related to one or moreother data assets that the personal data will be transferred to; and/or(6) one or more questions related to who will have the ability to accessand/or use the personal data.

Continuing to FIG. 16, an exemplary screen display 1600 depicts alisting of assets 1610 for a particular entity. These may, for example,have been identified as part of the data model generation systemdescribed above. As may be understood from this figure, a user mayselect a drop down indicator 1615 to view more information about aparticular asset. In the exemplary embodiment shown in FIG. 16, thesystem stores the managing organization group for the “New Asset”, butis missing some additional information (e.g., such as a description 1625of the asset). In order to fill out the missing inventory attributes forthe “New Asset”, the system, in particular embodiments, is configured toenable a user to select a Send Assessment indicia 1620 in order totransmit an assessment related to the selected asset to an individualtasked with providing one or more pieces of information related to theasset (e.g., a manager, or other individual with knowledge of the one ormore inventory attributes).

In response to the user selecting the Send Assessment indicia 1620, thesystem may create the assessment based at least in part on a templateassociated with the asset, and transmit the assessment to a suitableindividual for completion (e.g., and/or transmit a request to theindividual to complete the assessment).

FIG. 17 depicts an exemplary assessment transmission interface 1700 viawhich a user can transmit one or more assessments for completion. Asshown in this figure, the user may assign a respondent, provide adeadline, indicate a reminder time, and provide one or more commentsusing an assessment request interface 1710. The user may then select aSend Assessment(s) indicia 1720 in order to transmit the assessment.

FIG. 18 depicts an exemplary assessment 1800 which a user may encounterin response to receiving a request to complete the assessment asdescribed above with respect to FIGS. 16 and 17. As shown in FIG. 18,the assessment 1800 may include one or more questions that map to theone or more unpopulated attributes for the asset shown in FIG. 16. Forexample, the one or more questions may include a question related to adescription of the asset, which may include a free form text box 1820for providing a description of the asset. FIG. 19 depicts an exemplaryscreen display 1900 with the text box 1920 completed, where thedescription includes a value of “Value_1”. As shown in FIGS. 18 and 19,the user may have renamed “New Asset” (e.g., which may have included adefault or placeholder name) shown in FIGS. 16 and 17 to “7^(th) Asset.”

Continuing to FIG. 20, the exemplary screen display 2000 depicts thelisting of assets 2010 from FIG. 16 with some additional attributespopulated. For example, the Description 2025 (e.g., “Value_1”) providedin FIG. 19 has been added to the inventory. As may be understood inlight of this disclosure, in response to a user providing thedescription via the assessment shown in FIGS. 18 and 19, the system maybe configured to map the provided description to the attribute valueassociated with the description of the asset in the data inventory. Thesystem may have then modified the data inventory for the asset toinclude the description attribute. In various embodiments, the system isconfigured to store the modified data inventory as part of a data model(e.g., in computer memory).

FIGS. 21-24 depict exemplary screen displays showing exemplary questionsthat make up part of a processing activity questionnaire (e.g.,assessment). FIG. 21 depicts an exemplary interface 2100 for respondingto a first question 2110 and a second question 2120. As shown in FIG.21, the first question 2110 relates to whether the processing activityis a new or existing processing activity. The first question 2110 shownin FIG. 21 is a multiple choice question. The second question 2120relates to whether the organization is conducting the activity on behalfof another organization. As shown in this figure, the second question2120 includes both a multiple choice portion and a free-form responseportion.

As discussed above, in various embodiments, the system may be configuredto modify a questionnaire in response to (e.g., based on) one or moreresponses provided by a user completing the questionnaire. In particularembodiments, the system is configured to modify the questionnairesubstantially on-the-fly (e.g., as the user provides each particularanswer). FIG. 22 depicts an interface 2200 that includes a secondquestion 2220 that differs from the second question 2120 shown in FIG.21. As may be understood in light of this disclosure, in response to theuser providing a response to the first question 2110 in FIG. 21 thatindicates that the processing activity is a new processing activity, thesystem may substantially automatically modify the second question 2120from FIG. 21 to the second question 2220 from FIG. 22 (e.g., such thatthe second question 2220 includes one or more follow up questions orrequests for additional information based on the response to the firstquestion 2110 in FIG. 21).

As shown in FIG. 22, the second question 2220 requests a description ofthe activity that is being pursued. In various embodiments (e.g., suchas if the user had selected that the processing activity was an existingone), the system may not modify the questionnaire to include the secondquestion 2220 from FIG. 22, because the system may already storeinformation related to a description of the processing activity atissue. In various embodiments, any suitable question described hereinmay include a tooltip 2225 on a field name (e.g., which may provide oneor more additional pieces of information to guide a user's response tothe questionnaire and/or assessment).

FIGS. 23 and 24 depict additional exemplary assessment questions. Thequestions shown in these figures relate to, for example, particular dataelements processed by various aspects of a processing activity.

FIG. 25 depicts a dashboard 2500 that includes an accounting of one ormore assessments that have been completed, are in progress, or requirecompletion by a particular organization. The dashboard 2500 shown inthis figure is configured to provide information relate to the status ofone or more outstanding assessments. As may be understood in light ofthis disclosure, because of the volume of assessment requests, it may benecessary to utilize one or more third party organizations to facilitatea timely completion of one or more assessment requests. In variousembodiments, the dashboard may indicate that, based on a fact that anumber of assessments are still in progress or incomplete, that aparticular data model for an entity, data asset, processing activity,etc. remains incomplete. In such embodiments, an incomplete nature of adata model may raise one or more flags or indicate a risk that an entitymay not be in compliance with one or more legal or industry requirementsrelated to the collection, storage, and/or processing of personal data.

Intelligent Identity Scanning Module

Turning to FIG. 26, in particular embodiments, the Intelligent IdentityScanning Module 2600 is configured to scan one or more data sources toidentify personal data stored on one or more network devices for aparticular organization, analyze the identified personal data, andclassify the personal data (e.g., in a data model) based at least inpart on a confidence score derived using one or more machine learningtechniques. The confidence score may be and/or comprise, for example, anindication of the probability that the personal data is actuallyassociated with a particular data subject (e.g., that there is at leastan 80% confidence level that a particular phone number is associatedwith a particular individual.)

When executing the Intelligent Identity Scanning Module 2600, the systembegins, at Step 2610, by connecting to one or more databases or otherdata structures, and scanning the one or more databases to generate acatalog of one or more individuals and one or more pieces of personalinformation associated with the one or more individuals. The system may,for example, be configured to connect to one or more databasesassociated with a particular organization (e.g., one or more databasesthat may serve as a storage location for any personal or other datacollected, processed, etc. by the particular organization, for example,as part of a suitable processing activity. As may be understood in lightof this disclosure, a particular organization may use a plurality of oneor more databases (e.g., the One or More Databases 140 shown in FIG. 1),a plurality of servers (e.g., the One or More Third Party Servers 160shown in FIG. 1), or any other suitable data storage location in orderto store personal data and other data collected as part of any suitableprivacy campaign, privacy impact assessment, processing activity, etc.

In particular embodiments, the system is configured to scan the one ormore databases by searching for particular data fields comprising one ormore pieces of information that may include personal data. The systemmay, for example, be configured to scan and identify one of more piecesof personal data such as: (1) name; (2) address; (3) telephone number;(4) e-mail address; (5) social security number; (6) informationassociated with one or more credit accounts (e.g., credit card numbers);(7) banking information; (8) location data; (9) internet search history;(10) non-credit account data; and/or (11) any other suitable personalinformation discussed herein. In particular embodiments, the system isconfigured to scan for a particular type of personal data (e.g., or oneor more particular types of personal data).

The system may, in various embodiments, be further configured togenerate a catalog of one or more individuals that also includes one ormore pieces of personal information (e.g., personal data) identified forthe individuals during the scan. The system may, for example, inresponse to discovering one or more pieces of personal data in aparticular storage location, identify one or more associations betweenthe discovered pieces of personal data. For example, a particulardatabase may store a plurality of individuals' names in association withtheir respective telephone numbers. One or more other databases mayinclude any other suitable information.

The system may, for example, generate the catalog to include anyinformation associated with the one or more individuals identified inthe scan. The system may, for example, maintain the catalog in anysuitable format (e.g., a data table, etc.).

In still other embodiments, in addition to connecting to a database, thesystem may be configured to: (1) access an application through one ormore application programming interfaces (APIs); (2) use one or morescreen scraping techniques on an end user page to identify and analyzeeach field on the page; and/or (3) connect to any other suitable datastructure in order to generate the catalog of individuals and personalinformation associated with each of the individuals. In someembodiments, the system may be configured to analyze one or more accesslogs and applications set up through a system active directory or SSOportal for which one or more applications might contain certain data foruser groups. The system may then be configured to analyze an emailenvironment to identify one or more links to particular businessapplications, which may, for example, be in use by an entity and containcertain data. In still other embodiments, the system may be configuredto analyze one or more system log files (Syslog) from a securityenvironment to capture which particular applications an entity may beusing in order to discover such applications.

Continuing to Step 2620, the system is configured to scan one or morestructured and/or unstructured data repositories based at least in parton the generated catalog to identify one or more attributes of dataassociated with the one or more individuals. The system may, forexample, be configured to utilize information discovered during theinitial scan at Step 2610 to identify the one or more attributes of dataassociated with the one or more individuals.

For example, the catalog generated at Step 2610 may include a name,address, and phone number for a particular individual. The system may beconfigured, at Step 2620, to scan the one or more structured and/orunstructured data repositories to identify one or more attributes thatare associated with one or more of the particular individual's name,address and/or phone number. For example, a particular data repositorymay store banking information (e.g., a bank account number and routingnumber for the bank) in association with the particular individual'saddress. In various embodiments, the system may be configured toidentify the banking information as an attribute of data associated withthe particular individual. In this way, the system may be configured toidentify particular data attributes (e.g., one or more pieces ofpersonal data) stored for a particular individual by identifying theparticular data attributes using information other than the individual'sname.

Returning to Step 2630, the system is configured to analyze andcorrelate the one or more attributes and metadata for the scanned one ormore structured and/or unstructured data repositories. In particularembodiments, the system is configured to correlate the one or moreattributes with metadata for the associated data repositories from whichthe system identified the one or more attributes. In this way, thesystem may be configured to store data regarding particular datarepositories that store particular data attributes.

In particular embodiments, the system may be configured tocross-reference the data repositories that are discovered to store oneor more attributes of personal data associated with the one or moreindividuals with a database of known data assets. In particularembodiments, the system is configured to analyze the data repositoriesto determine whether each data repository is part of an existing datamodel of data assets that collect, store, and/or process personal data.In response to determining that a particular data repository is notassociated with an existing data model, the system may be configured toidentify the data repository as a new data asset (e.g., via assetdiscovery), and take one or more actions (e.g., such as any suitableactions described herein) to generate and populate a data model of thenewly discovered data asset. This may include, for example: (1)generating a data inventory for the new data asset; (2) populating thedata inventory with any known attributes associated with the new dataasset; (3) identifying one or more unpopulated (e.g., unknown)attributes of the data asset; and (4) taking any suitable actiondescribed herein to populate the unpopulated data attributes.

In particular embodiments, the system my, for example: (1) identify asource of the personal data stored in the data repository that led tothe new asset discovery; (2) identify one or more relationships betweenthe newly discovered asset and one or more known assets; and/or (3) etc.

Continuing to Step 2640, the system is configured to use one or moremachine learning techniques to categorize one or more data elements fromthe generated catalog, analyze a flow of the data among the one or moredata repositories, and/or classify the one or more data elements basedon a confidence score as discussed below.

Continuing to Step 2650, the system, in various embodiments, isconfigured to receive input from a user confirming or denying acategorization of the one or more data elements, and, in response,modify the confidence score. In various embodiments, the system isconfigured to iteratively repeat Steps 2640 and 2650. In this way, thesystem is configured to modify the confidence score in response to auser confirming or denying the accuracy of a categorization of the oneor more data elements. For example, in particular embodiments, thesystem is configured to prompt a user (e.g., a system administrator,privacy officer, etc.) to confirm that a particular data element is, infact, associated with a particular individual from the catalog. Thesystem may, in various embodiments, be configured to prompt a user toconfirm that a data element or attribute discovered during one or moreof the scans above were properly categorized at Step 2640.

In particular embodiments, the system is configured to modify theconfidence score based at least in part on receiving one or moreconfirmations that one or more particular data elements or attributesdiscovered in a particular location during a scan are associated withparticular individuals from the catalog. As may be understood in lightof this disclosure, the system may be configured to increase theconfidence score in response to receiving confirmation that particulartypes of data elements or attributes discovered in a particular storagelocation are typically confirmed as being associated with particularindividuals based on one or more attributes for which the system wasscanning.

Exemplary Intelligent Identity Scanning Technical Platforms

FIG. 27 depicts an exemplary technical platform via which the system mayperform one or more of the steps described above with respect to theIntelligent Identity Scanning Module 2600. As shown in the embodiment inthis figure, an Intelligent Identity Scanning System 2600 comprises anIntelligent Identity Scanning Server 130, such as the IntelligentIdentity Scanning Server 130 described above with respect to FIG. 1. TheIntelligent Identity Scanning Server 130 may, for example, comprise aprocessing engine (e.g., one or more computer processors). In someembodiments, the Intelligent Identity Scanning Server 130 may includeany suitable cloud hosted processing engine (e.g., one or morecloud-based computer servers). In particular embodiments, theIntelligent Identity Scanning Server 130 is hosted in a Microsoft Azurecloud.

In particular embodiments, the Intelligent Identity Scanning Server 130is configured to sit outside one or more firewalls (e.g., such as thefirewall 195 shown in FIG. 26). In such embodiments, the IntelligentIdentity Scanning Server 130 is configured to access One or More RemoteComputing Devices 150 through the Firewall 195 (e.g., one or morefirewalls) via One or More Networks 115 (e.g., such as any of the One orMore Networks 115 described above with respect to FIG. 1).

In particular embodiments, the One or More Remote Computing Devices 150include one or more computing devices that make up at least a portion ofone or more computer networks associated with a particular organization.In particular embodiments, the one or more computer networks associatedwith the particular organization comprise one or more suitable servers,one or more suitable databases, one or more privileged networks, and/orany other suitable device and/or network segment that may store and/orprovide for the storage of personal data. In the embodiment shown inFIG. 27, the one or more computer networks associated with theparticular organization may comprise One or More Third Party Servers160, One or More Databases 140, etc. In particular embodiments, the Oneor More Remote Computing Devices 150 are configured to access one ormore segments of the one or more computer networks associated with theparticular organization. In some embodiments, the one or more computernetworks associated with the particular organization comprise One orMore Privileged Networks 165. In still other embodiments, the one ormore computer networks comprise one or more network segments connectedvia one or more suitable routers, one or more suitable network hubs, oneor more suitable network switches, etc.

As shown in FIG. 27, various components that make up one or more partsof the one or more computer networks associated with the particularorganization may store personal data (e.g., such as personal data storedon the One or More Third Party Servers 160, the One or More Databases140, etc.). In various embodiments, the system is configured to performone or more steps related to the Intelligent Identity Scanning Server2600 in order to identify the personal data for the purpose ofgenerating the catalog of individuals described above (e.g., and/oridentify one or more data assets within the organization's network thatstore personal data)

As further shown in FIG. 27, in various embodiments, the One or MoreRemote Computing Devices 150 may store a software application (e.g., theIntelligent Identity Scanning Module). In such embodiments, the systemmay be configured to provide the software application for installationon the One or More Remote Computing Devices 150. In particularembodiments, the software application may comprise one or more virtualmachines. In particular embodiments, the one or more virtual machinesmay be configured to perform one or more of the steps described abovewith respect to the Intelligent Identity Scanning Module 2600 (e.g.,perform the one or more steps locally on the One or More RemoteComputing Devices 150).

In various embodiments, the one or more virtual machines may have thefollowing specifications: (1) any suitable number of cores (e.g., 4, 6,8, etc.); (2) any suitable amount of memory (e.g., 4 GB, 8 GB, 16 GBetc.); (3) any suitable operating system (e.g., CentOS 7.2); and/or (4)any other suitable specification. In particular embodiments, the one ormore virtual machines may, for example, be used for one or more suitablepurposes related to the Intelligent Identity Scanning System 2700. Theseone or more suitable purposes may include, for example, running any ofthe one or more modules described herein, storing hashed and/ornon-hashed information (e.g., personal data, personally identifiabledata, catalog of individuals, etc.), storing and running one or moresearching and/or scanning engines (e.g., Elasticsearch), etc.

In various embodiments, the Intelligent Identity Scanning System 2700may be configured to distribute one or more processes that make up partof the Intelligent Identity Scanning Process (e.g., described above withrespect to the Intelligent Identity Scanning Module 1800). The one ormore software applications installed on the one or more Remote ComputingDevices 150 may, for example, be configured to provide access to the oneor more computer networks associated with the particular organization tothe Intelligent Identity Scanning Server 130. The system may then beconfigured to receive, from the One or more Remote Computing Devices 150at the Intelligent Identity Scanning Server 130, via the Firewall 195and One or More Networks 115, scanned data for analysis.

In particular embodiments, the Intelligent Identity Scanning System 2700is configured to reduce an impact on a performance of the One or MoreRemote Computing Devices 150, One or More Third Party Servers 160 andother components that make up one or more segments of the one or morecomputer networks associated with the particular organization. Forexample, in particular embodiments, the Intelligent Identity ScanningSystem 2700 may be configured to utilize one or more suitable bandwidththrottling techniques. In other embodiments, the Intelligent IdentityScanning System 2700 is configured to limit scanning (e.g., any of theone or more scanning steps described above with respect to theIntelligent Identity Scanning Module 2600) and other processing steps(e.g., one or more steps that utilize one or more processing resources)to non-peak times (e.g., during the evening, overnight, on weekendsand/or holidays, etc.). In other embodiments, the system is configuredto limit performance of such processing steps to backup applications anddata storage locations. The system may, for example, use one or moresampling techniques to decrease a number of records required to scanduring the personal data discovery process.

FIG. 28 depicts an exemplary asset access methodology that the systemmay utilize in order to access one or more network devices that maystore personal data (e.g., or other personally identifiableinformation). As may be understood from this figure, the system may beconfigured to access the one or more network devices using a locallydeployed software application (e.g., such as the software applicationdescribed immediately above). In various embodiments, the softwareapplication is configured to route identity scanning traffic through oneor more gateways, configure one or more ports to accept one or moreidentity scanning connections, etc.

As may be understood from this figure, the system may be configured toutilize one or more credential management techniques to access one ormore privileged network portions. The system may, in response toidentifying particular assets or personally identifiable information viaa scan, be configured to retrieve schema details such as, for example,an asset ID, Schema ID, connection string, credential reference URL,etc. In this way, the system may be configured to identify and store alocation of any discovered assets or personal data during a scan.

Data Subject Access Request Fulfillment Module

Turning to FIG. 29, in particular embodiments, a Data Subject AccessRequest Fulfillment Module 2900 is configured to receive a data subjectaccess request, process the request, and fulfill the request based atleast in part on one or more request parameters. In various embodiments,an organization, corporation, etc. may be required to provideinformation requested by an individual for whom the organization storespersonal data within a certain time period (e.g., 30 days). As aparticular example, an organization may be required to provide anindividual with a listing of, for example: (1) any personal data thatthe organization is processing for an individual, (2) an explanation ofthe categories of data being processed and the purpose of suchprocessing; and/or (3) categories of third parties to whom the data maybe disclosed.

Various privacy and security policies (e.g., such as the EuropeanUnion's General Data Protection Regulation, and other such policies) mayprovide data subjects (e.g., individuals, organizations, or otherentities) with certain rights related to the data subject's personaldata that is collected, stored, or otherwise processed by anorganization. These rights may include, for example: (1) a right toobtain confirmation of whether a particular organization is processingtheir personal data; (2) a right to obtain information about the purposeof the processing (e.g., one or more reasons for which the personal datawas collected); (3) a right to obtain information about one or morecategories of data being processed (e.g., what type of personal data isbeing collected, stored, etc.); (4) a right to obtain information aboutone or more categories of recipients with whom their personal data maybe shared (e.g., both internally within the organization or externally);(5) a right to obtain information about a time period for which theirpersonal data will be stored (e.g., or one or more criteria used todetermine that time period); (6) a right to obtain a copy of anypersonal data being processed (e.g., a right to receive a copy of theirpersonal data in a commonly used, machine-readable format); (7) a rightto request erasure (e.g., the right to be forgotten), rectification(e.g., correction or deletion of inaccurate data), or restriction ofprocessing of their personal data; and (8) any other suitable rightsrelated to the collection, storage, and/or processing of their personaldata (e.g., which may be provided by law, policy, industry ororganizational practice, etc.).

As may be understood in light of this disclosure, a particularorganization may undertake a plurality of different privacy campaigns,processing activities, etc. that involve the collection and storage ofpersonal data. In some embodiments, each of the plurality of differentprocessing activities may collect redundant data (e.g., may collect thesame personal data for a particular individual more than once), and maystore data and/or redundant data in one or more particular locations(e.g., on one or more different servers, in one or more differentdatabases, etc.). In this way, a particular organization may storepersonal data in a plurality of different locations which may includeone or more known and/or unknown locations. As such, complying withparticular privacy and security policies related to personal data (e.g.,such as responding to one or more requests by data subjects related totheir personal data) may be particularly difficult (e.g., in terms ofcost, time, etc.). In particular embodiments, a data subject accessrequest fulfillment system may utilize one or more data model generationand population techniques (e.g., such as any suitable techniquedescribed herein) to create a centralized data map with which the systemcan identify personal data stored, collected, or processed for aparticular data subject, a reason for the processing, and any otherinformation related to the processing.

Turning to FIG. 21, when executing the Data Subject Access RequestModule 2100, the system begins, at Step 2110, by receiving a datasubject access request. In various embodiments, the system receives therequest via a suitable web form. In certain embodiments, the requestcomprises a particular request to perform one or more actions with anypersonal data stored by a particular organization regarding therequestor. For example, in some embodiments, the request may include arequest to view one or more pieces of personal data stored by the systemregarding the requestor. In other embodiments, the request may include arequest to delete one or more pieces of personal data stored by thesystem regarding the requestor. In still other embodiments, the requestmay include a request to update one or more pieces of personal datastored by the system regarding the requestor. In still otherembodiments, the request may include a request based on any suitableright afforded to a data subject, such as those discussed above.

Continuing to Step 2120, the system is configured to process the requestby identifying and retrieving one or more pieces of personal dataassociated with the requestor that are being processed by the system.For example, in various embodiments, the system is configured toidentify any personal data stored in any database, server, or other datarepository associated with a particular organization. In variousembodiments, the system is configured to use one or more data models,such as those described above, to identify this personal data andsuitable related information (e.g., where the personal data is stored,who has access to the personal data, etc.). In various embodiments, thesystem is configured to use intelligent identity scanning (e.g., asdescribed above) to identify the requestor's personal data and relatedinformation that is to be used to fulfill the request.

In still other embodiments, the system is configured to use one or moremachine learning techniques to identify such personal data. For example,the system may identify particular stored personal data based on, forexample, a country in which a website that the data subject request wassubmitted is based, or any other suitable information.

In particular embodiments, the system is configured to scan and/orsearch one or more existing data models (e.g., one or more current datamodels) in response to receiving the request in order to identify theone or more pieces of personal data associated with the requestor. Thesystem may, for example, identify, based on one or more data inventories(e.g., one or more inventory attributes) a plurality of storagelocations that store personal data associated with the requestor. Inother embodiments, the system may be configured to generate a data modelor perform one or more scanning techniques in response to receiving therequest (e.g., in order to automatically fulfill the request).

Returning to Step 2130, the system is configured to take one or moreactions based at least in part on the request. In some embodiments, thesystem is configured to take one or more actions for which the requestwas submitted (e.g., display the personal data, delete the personaldata, correct the personal data, etc.). In particular embodiments, thesystem is configured to take the one or more actions substantiallyautomatically. In particular embodiments, in response a data subjectsubmitting a request to delete their personal data from anorganization's systems, the system may: (1) automatically determinewhere the data subject's personal data is stored; and (2) in response todetermining the location of the data (which may be on multiple computingsystems), automatically facilitate the deletion of the data subject'spersonal data from the various systems (e.g., by automatically assigninga plurality of tasks to delete data across multiple business systems toeffectively delete the data subject's personal data from the systems).In particular embodiments, the step of facilitating the deletion maycomprise, for example: (1) overwriting the data in memory; (2) markingthe data for overwrite; (2) marking the data as free (e.g., and deletinga directory entry associated with the data); and/or (3) any othersuitable technique for deleting the personal data. In particularembodiments, as part of this process, the system uses an appropriatedata model (see discussion above) to efficiently determine where all ofthe data subject's personal data is stored.

Data Subject Access Request User Experience

FIGS. 30-31 depict exemplary screen displays that a user may view whensubmitting a data subject access request. As shown in FIG. 30, a website30000 associated with a particular organization may include auser-selectable indicia 3005 for submitting a privacy-related request. Auser desiring to make such a request may select the indicia 3005 inorder to initiate the data subject access request process.

FIG. 31 depicts an exemplary data subject access request form in both anunfilled and filled out state. As shown in this figure, the system mayprompt a user to provide information such as, for example: (1) what typeof requestor the user is (e.g., employee, customer, etc.); (2) what therequest involves (e.g., requesting info, opting out, deleting data,updating data, etc.); (3) first name; (4) last name; (5) email address;(6) telephone number; (7) home address; and/or (8) one or more detailsassociated with the request.

As discussed in more detail above, a data subject may submit a subjectaccess request, for example, to request a listing of any personalinformation that a particular organization is currently storingregarding the data subject, to request that the personal data bedeleted, to opt out of allowing the organization to process the personaldata, etc.

Alternative Embodiments

In particular embodiments, a data modeling or other system describedherein may include one or more features in addition to those described.Various such alternative embodiments are described below.

Processing Activity and Data Asset Assessment Risk Flagging

In particular embodiments, the questionnaire template generation systemand assessment system described herein may incorporate one or more riskflagging systems. FIGS. 32-35 depict exemplary user interfaces thatinclude risk flagging of particular questions within a processingactivity assessment. As may be understood from these figures, a user mayselect a flag risk indicia to provide input related to a description ofrisks and mitigation of a risk posed by one or more inventory attributesassociated with the question. As shown in these figures, the system maybe configured to substantially automatically assign a risk to aparticular response to a question in a questionnaire. In variousembodiments, the assigned risk is determined based at least in part onthe template from which the assessment was generated.

In particular embodiments, the system may utilize the risk levelassigned to particular questionnaire responses as part of a riskanalysis of a particular processing activity or data asset. Varioustechniques for assessing the risk of various privacy campaigns aredescribed in U.S. patent application Ser. No. 15/256,419, filed Sep. 2,2016, entitled “Data processing systems and methods for operationalizingprivacy compliance and assessing the risk of various respective privacycampaigns,” which is hereby incorporated herein in its entirety.

Cross-Border Visualization Generation System

In particular embodiments, a Cross-Border Visualization GenerationSystem is configured to analyze one or more data systems (e.g., dataassets), identify data transfers between/among those systems, determinewhether any particular regulations apply to the identified datatransfers, and generate a visual representation of physical locations ofthe one or more data systems and the one or more data transfers betweenthem. The system may, for example, color-code one or more lines orindicators showing a transfer of data between a first and second datasystem. The one or more indicators may convey, for example: (1) whetherthe data transfer is secure; (2) a type or level of security that isapplied to the transfers; (3) one or more regulations that apply to thetransfer; and/or (4) any other suitable information related to thetransfer of particular data between the first and second data system.

Various processes performed by the Cross-Border Visualization GenerationSystem may be implemented by a Cross-Border Visualization GenerationModule 3600. Referring to FIG. 36, in particular embodiments, thesystem, when executing the Cross-Border Visualization Generation Module3600, is configured to: (1) identify data systems associated with aparticular entity; (2) determine a location of the data systems; (3)identity one or more transfers of particular data elements betweenparticular data systems of the one or more data systems; (4) determineone or more regulations that relate to the one or more data transfers;and (5) generate a visual representation of the one or more datatransfers based at least in part on the one or more regulations.

When executing the Cross-Border Visualization Generation Module 3600,the system begins, at Step 3610, by identifying one or more data systems(e.g., data assets) associated with a particular entity. The particularentity may include, for example, a particular organization, company,sub-organization, etc. In particular embodiments, the one or more dataassets (e.g., data systems) may include, for example, any entity thatcollects, processes, contains, and/or transfers data (e.g., such as asoftware application, “internet of things” computerized device,database, website, data-center, server, etc.). For example, a first dataasset may include any software or device utilized by a particular entityfor such data collection, processing, transfer, storage, etc. In variousembodiments, the first data asset may be at least partially stored onand/or physically located in a particular location. For example, aserver may be located in a particular country, jurisdiction, etc. Apiece of software may be stored on one or more servers in a particularlocation, etc.

In particular embodiments, the system is configured to identify the oneor more data systems using one or more data modeling techniques. Asdiscussed more fully above, a data model may store the followinginformation: (1) the entity that owns and/or uses a particular dataasset (e.g., such as a primary data asset, an example of which is shownin the center of the data model in FIG. 4); (2) one or more departmentswithin the organization that are responsible for the data asset; (3) oneor more software applications that collect data (e.g., personal data)for storage in and/or use by the data asset; (4) one or more particulardata subjects (or categories of data subjects) that information iscollected from for use by the data asset; (5) one or more particulartypes of data that are collected by each of the particular applicationsfor storage in and/or use by the data asset; (6) one or more individuals(e.g., particular individuals or types of individuals) that arepermitted to access and/or use the data stored in, or used by, the dataasset; (7) which particular types of data each of those individuals areallowed to access and use; and (8) one or more data assets (destinationassets) that the data is transferred to for other use, and whichparticular data is transferred to each of those data assets.

As may be understood in light of this disclosure, the system may utilizea data model (e.g., or one or more data models) of data assetsassociated with a particular entity to identify the one or more datasystems associated with the particular entity.

Continuing to Step 3620, the system is configured to analyze the one ormore data assets (e.g., data systems) to identify one or more dataelements stored in the one or more identified data systems. Inparticular embodiments, the system is configured to identify one or moredata elements stored by the one or more data systems that are subject totransfer (e.g., transfer to the one or more data systems such as from asource asset, transfer from the one or more data systems to adestination asset, etc.). In particular embodiments, the system isconfigured to identify a particular data element that is subject to suchtransfer (e.g., such as a particular piece of personal data or otherdata). In some embodiments, the system may be configured to identify anysuitable data element that is subject to transfer and includes personaldata. The system may be configured to identify such transfer data usingany suitable technique described herein.

In any embodiment described herein, personal data may include, forexample: (1) the name of a particular data subject (which may be aparticular individual); (2) the data subject's address; (3) the datasubject's telephone number; (4) the data subject's e-mail address; (5)the data subject's social security number; (6) information associatedwith one or more of the data subject's credit accounts (e.g., creditcard numbers); (7) banking information for the data subject; (8)location data for the data subject (e.g., their present or pastlocation); (9) internet search history for the data subject; and/or (10)any other suitable personal information, such as other personalinformation discussed herein.

As may be understood from this disclosure, the transfer of personal datamay trigger one or more regulations that govern such transfer. Inparticular embodiments, personal data may include any data which relateto a living individual who can be identified: (1) from the data; or (2)from the data in combination with other information which is in thepossession of, or is likely to come into the possession of a particularentity. In particular embodiments, a particular entity may collect,store, process, and/or transfer personal data for one or more customers,one or more employees, etc.

In various embodiments, the system is configured to use one or more datamodels of the one or more data assets (e.g., data systems) to analyzeone or more data elements associated with those assets to determinewhether the one or more data elements include one or more data elementsthat include personal data and are subject to transfer. In particularembodiments, the transfer may include, for example: (1) an internaltransfer (e.g., a transfer from a first data asset associated with theentity to a second data asset associated with the entity); (2) anexternal transfer (e.g., a transfer from a data asset associated withthe entity to a second data asset associated with a second entity);and/or (3) a collective transfer (e.g., a transfer to a data assetassociated with the entity from an external data asset associated with asecond entity).

Next, at Step 3630, the system is configured to define a plurality ofphysical locations and identify, for each of the one or more datasystems, a particular physical location of the plurality of physicallocations. In some embodiments, the system is configured to define theplurality of physical locations based at least in part on input from auser. The system may, for example, define each of the plurality ofphysical locations based at least in part on one or more geographicboundaries. These one or more geographic boundaries may include, forexample: (1) one or more countries; (2) one or more continents; (3) oneor more jurisdictions (e.g., such as one or more legal jurisdictions);(4) one or more territories; (5) one or more counties; (6) one or morecities; (7) one or more treaty members (e.g., such as members of atrade, defense, or other treaty); and/or (8) any other suitablegeographically distinct physical locations.

The system may then be configured to identify, for each of the one ormore data systems identified at Step 3610, an associated physicallocation. For example, the system may be configured to determine inwhich of the one or more defined plurality of physical locations eachparticular data system is physically located. In particular embodiments,the system is configured to determine the physical location based atleast in part on one or more data attributes of a particular data asset(e.g., data system) using one or more data modeling techniques (e.g.,using one or more suitable data modeling techniques described herein).In some embodiments, the system may be configured to determine thephysical location of each data asset based at least in part on anexisting data model that includes the data asset. In still otherembodiments, the system may be configured to determine the physicallocation based at least in part on an IP address and/or domain of thedata asset (e.g., in the case of a computer server or other computingdevice) or any other identifying feature of a particular data asset.

Returning to Step 3640, the system is configured to analyze theidentified one or more data elements to determine one or more datatransfers between one or more data systems in different particularphysical locations. The system may, for example, analyze a data modelbased on each particular data asset to identify one or more datatransfers between and/or among the one or more data assets (e.g., datasystems). For example, as may be understood from FIG. 4, a particularasset (e.g., storage asset) may receive data, for example, from a datasubject, a collection asset, or other suitable source (e.g., dataasset). The particular asset may further, in some embodiments, transferdata to a transfer asset (e.g., an asset to which the particular assettransfers data). The system may be configured to identify such datatransfers between and/or among one or more data assets for the purposeof generating a visual representation of such data transfers.

Continuing to Step 3650, the system is configured to determine one ormore regulations that relate to (e.g., apply to) the one or more datatransfers. As may understood in light of this disclosure, one or moreregulations (e.g., industry regulations, legal regulations, etc.) maygovern the transfer of personal data (e.g., between one or morejurisdictions, physical locations, and the like). In particular, the oneor more regulations may impose one or more minimum standards on thehandling of the transfer of such personal data in the interest ofprotecting the privacy of one or more data subjects or other individualswith whom the personal data is associated. In particular instances, itmay be inevitable (e.g., as a result of the sharing of customer data,the centralization of IT services, etc.) that a particular entity orcompany (e.g., a particular entity whose business activities span aplurality of jurisdictions or locations) will undertake one or more datatransfers that may triggers the one or more regulations.

In particular embodiments, the one or more regulations described abovemay include one or more transfer restrictions. In various embodiments,the one or more transfer restrictions may restrict transfer from a firstlocation (e.g., jurisdiction) to a second location (e.g., jurisdiction)absent an adequate level of privacy protection. A particular exemplarytransfer restriction may, for example, require data transferred from afirst location to a second location to be subject to the same level ofprivacy protection at the second location that the data enjoys in thefirst location. For example, the first location may, for example, placeany suitable limit on the collection and storage of personal data (e.g.,one or more time limits, one or more encryption requirements, etc.). Inparticular embodiments, the one or more regulations may include atransfer restriction that prohibits transfer of personal data from thefirst location to a second location unless the second location placeslimits on the collection and storage of personal data that are at leastas stringent as the first location.

In various embodiments, the system may, for example: (1) analyze one ormore first storage restrictions on personal data stored in a first dataasset; (2) analyze one or more second storage restrictions on personaldata stored in a second data asset to which the first data assettransfers personal data; and (3) compare the one or more first storagerestrictions with the one or more second storage restrictions. Thesystem may then, for example, flag a transfer of data from the firstdata asset to the second data asset based at least in part on thecomparison. For example, in response to determining that the one or moresecond restrictions are less stringent than the one or more firstrestrictions, the system may flag the transfer as risky or noncompliant.In another example, in response to determining that the one or moresecond restrictions are at least as stringent as the one or more firstrestrictions, the system may flag (e.g., automatically flag) thetransfer as acceptable or compliant.

In particular embodiments, the system may be configured to substantiallyautomatically determine that a transfer to a particular location isadequate. The system may, for example, store a listing (e.g., in memory)of one or more locations (e.g., countries) deemed automatically adequateas destinations of transferred personal data. In such embodiments, theone or more regulations may include a regulation that any location onthe ‘safe list’ provides adequate privacy protection for personal data.The system may then substantially automatically determine that atransfer of data that includes a ‘safe list’ location as a targetdestination in a transfer would automatically meet an adequacy standardfor data transfer. In a particular example, the one or more locations onthe ‘safe list’ may include one or more countries (e.g., Argentina,Canada, Israel, Switzerland, Uruguay, Jersey, Guernsey, the Isle of Man,etc.).

In various other embodiments, the one or more regulations may include aregulation that a transfer of personal data to a location that is partof a safe harbor is acceptable. In various embodiments, a safe harbormay include a commitment to adhere to a set of safe harbor principlesrelated to data protection. In a particular example, a United Statescompany wishing to identify as a safe harbor entity may be required toself-certify to the U.S. Department of Commerce that it adheres to theSafe Harbor principles and to make a public declaration of theadherence.

In particular other embodiments, the system may identify a particularprivacy shield arrangement between a first and second location in orderto determine an adequacy of a transfer of data from the first locationto the second location. In particular, a privacy shield arrangement mayfacilitate monitoring of an entity's compliance with one or morecommitments and enforcement of those commitments under the privacyshield. In particular, an entity entering a privacy shield arrangementmay, for example: (1) be obligated to publicly commit to robustprotection of any personal data that it handles; (2) be required toestablish a clear set of safeguards and transparency mechanisms on whocan access the personal data it handles; and/or (3) be required toestablish a redress right to address complaints about improper access tothe personal data.

In a particular example of a privacy shield, a privacy shield betweenthe United States and Europe may involve, for example: (1) establishmentof responsibility by the U.S. Department of Commerce to monitor anentity's compliance (e.g., a company's compliance) with its commitmentsunder the privacy shield; and (2) establishment of responsibility of theFederal Trade Commission having enforcement authority over thecommitments. In a further example, the U.S. Department of Commerce maydesignate an ombudsman to hear complaints from Europeans regarding U.S.surveillance that affects personal data of Europeans.

In some embodiments, the one or more regulations may include aregulation that allows data transfer to a country or entity thatparticipates in a safe harbor and/or privacy shield as discussed herein.The system may, for example, be configured to automatically identify atransfer that is subject to a privacy shield and/or safe harbor as ‘lowrisk.’

In some embodiments, the one or more regulations may include aregulation that a location that is not deemed automatically adequate asa data transfer target (e.g., a location to which data is beingtransferred) may be deemed adequate by entering one or more contracts(e.g., standard clauses) with an entity that is the source of thetransferred data. For example, the system may automatically determinethat a particular data transfer is adequate by identifying a contractthat exists between a first entity and a second entity, where the firstentity is transferring data from a first asset to a second assetassociated with the second entity. In various embodiments, the one ormore data elements that make up a data model (e.g., for the first dataasset) may indicate the existence of any contracts that the first entityhas executed related to the transfer of data with one or more otherentities. In various embodiments, the system is configured to analyzethe one or more contracts to determine whether the one or more contractsapply to a particular data transfer of the one or more transfersidentified at Step 3640.

In particular embodiments, the one or more contracts may include one ormore third party beneficiary rights to the one or more data subjectswhose personal data is subject to transfer. In such embodiments, suchcontracts may, for example, be enforced by an exporting entity (e.g.,the entity that is transferring the data) as well as the data subjectthemselves.

In particular embodiments, a further method of legitimizing a transferof data between one or more data assets may include implementing one ormore binding corporate rules. In particular embodiments, the one or morebinding corporate rules may be approved by a regulating authority. Insuch embodiments, the one or more regulations referred to in step 3650may include one or more regulations related to the existence of one ormore binding corporate rules (e.g., that have been approved by aregulating authority).

In various embodiments, the one or more binding corporate rules mayinclude a scheme that involves an entity (e.g., corporate group) settingup an internal suite of documents that set out how the entity intends toprovide adequate safeguards to individuals whose personal data is beingtransferred to a second location (e.g., country). In particularembodiments, the one or more binding corporate rules may include one ormore safeguards that are no less than those required by the location inwhich the personal data is originally stored.

At Step 3660, the system continues by generating a visual representationof the one or more data transfers based at least in part on the one ormore regulations. The system may, for example, generate a visualrepresentation of a map that includes the plurality of physicallocations described above. The system may then indicate, on the visualrepresentation, a location of each of the one or more data systems(e.g., using a suitable marker or indicia). In particular embodiments,the system may color code one or more of the plurality of physicallocations based on, for example, an existence of a privacy shield, aprevailing legal requirement for a particular jurisdiction, etc.

In various embodiments, the system may be configured to generate, on themap, a visual representation of a data transfer between at least a firstdata asset and a second data asset (e.g., where the first and seconddata asset are in two different physical locations). For example, thesystem may generate a linear representation of the transfer, or othersuitable representation. In particular embodiments, they system isconfigured to color code the visual representation of the transfer basedat least in part on the physical locations, one or more regulations,etc. In still other embodiments, the system is configured to color codethe visual representation of the transfer based at least in part on theone or more regulations that the system has determined apply to thetransfer (e.g., one or more binding corporate rules, privacy shield,etc.). This may, for example, indicate a legal basis of each particularidentified data transfer.

In various embodiments, the system may be configured to substantiallyautomatically flag a particular transfer of data as problematic (e.g.,because the transfer does not comply with an applicable regulation). Forexample, a particular regulation may require data transfers from a firstasset to a second asset to be encrypted. The system may determine, basedat least in part on the one or more data elements, that the transfer isnot encrypted. In response, the system may flag the transfer as Highrisk (e.g., using a particular color such as red). In various otherembodiments, the system may be configured to determine a risk level of aparticular transfer based at least in part on the physical location ofeach of the data assets, the one or more regulations, the type of databeing transferred (e.g., whether the data contains personal data), etc.

In particular embodiments, the visual representation may be used by aparticular entity to demonstrate compliance with respect to one or moreregulations related to the transfer of personal data. In suchembodiments, the visual representation may serve as a report thatindicates the legal basis of any transfer performed by the entity (e.g.,and further serve as documentation of the entity's compliance with oneor more legal regulations).

Risk Identification for Cross-Border Data Transfers

In various embodiments, the Cross-Border Visualization Generation Systemmay identify one or more risk associated with a cross-border datatransfer. In various embodiments, a data transfer record may be createdfor each transfer of data between a first asset in a first location anda second asset in a second location where the transfer record may alsoinclude information regarding the type of data being transferred, a timeof the data transfer, an amount of data being transferred, etc. Thesystem may apply data transfer rules to each data transfer record. Thedata transfer rules may be configurable to support different privacyframeworks (e.g., a particular data subject type is being transferredfrom a first asset in the European Union to a second asset outside ofthe European Union) and organizational frameworks (e.g., to support thedifferent locations and types of data assets within an organization).The applied data transfer rules may be automatically configured by thesystem (e.g., when an update is applied to privacy rules in a country orregion) or manually adjusted by the particular organization (e.g., by aprivacy officer of the organization). The data transfer rules to beapplied may vary based on the data being transferred. For example, ifthe data being transferred includes personal data, then particular datatransfer rules may be applied (e.g., encryption level requirements,storage time limitations, access restrictions, etc.).

In particular embodiments, the system may perform a data transferassessment on each data transfer record based on the data transfer rulesto be applied to each data transfer record. The data transfer assessmentperformed by the system may identify risks associated with the datatransfer record, and in some embodiments, a risk score may be calculatedfor the data transfer. For example, a data transfer that containssensitive data that includes a customer credit card, has a sourcelocation in one continent (e.g., at a merchant), and has a destinationlocation in a different continent (e.g., in a database), may have a highrisk score because of the transfer of data between two separatecontinents and the sensitivity of the data being transferred.

The risk score may be calculated in any suitable way, and may includerisk factors such as a source location of the data transfer, adestination location of the data transfer, the type of data beingtransferred, a time of the data transfer, an amount of data beingtransferred, etc. Additionally, the system may apply weighting factors(e.g., custom weighting factors or automatically determined ones) to therisk factors. Further, in some implementation, the system can include athreshold risk score where a data transfer may be terminated (e.g.,automatically) if the data transfer risk score indicates a higher riskthan the threshold risk score (e.g., the data transfer risk score beinghigher than the threshold risk score). When the data transfer risk scoreindicates a lower risk than the threshold risk score, then the systemmay process the data transfer. In some implementations, if one or moreof the risk factors indicate a heightened risk for the data transfer,then the system can notify an individual associated with the particularorganization. For example, the individual associated with the particularorganization may enable the data transfer to process, flag the datatransfer for further evaluation (e.g., send the data transferinformation to another individual for input), or terminate the datatransfer, among other actions.

The system may process the data transfer after evaluating the datatransfer assessment and/or the risk score for the data transfer.Additionally, in some implementations, the system may initiate the datatransfer via a secure terminal or secure link between a computer systemof the source location and a computer system of the destination locationwhere the system to prevent interception of the data or unwarrantedaccess to the additional information.

Cross-Border Visualization Generation User Experience

FIGS. 37-38 depict exemplary screen displays that a user may view whenreviewing a cross-border visualization generated by the system asdescribed above. As shown in FIG. 37, the system may be configured togenerate a visual representation of an asset map (e.g., a data assetmap, data system map, etc.). As may be understood from this Figure, thesystem may be configured to generate a map that indicates a location ofone or more data assets for a particular entity. In the embodiment shownin this figure, locations that contain a data asset are indicated bycircular indicia that contain the number of assets present at thatlocation. In the embodiment shown in this figure, the locations arebroken down by country. In particular embodiments, the asset map maydistinguish between internal assets (e.g., first party servers, etc.)and external/third party assets (e.g., third party owned servers thatthe entity utilizes for data storage, transfer, etc.).

In some embodiments, the system is configured to indicate, via thevisual representation, whether one or more assets have an unknownlocation (e.g., because the data model described above may be incompletewith regard to the location). In such embodiments, the system may beconfigured to: (1) identify the asset with the unknown location; (2) useone or more data mapping techniques described herein to determine thelocation (e.g., pinging the asset); and (3) update a data modelassociated with the asset to include the location.

As shown in FIG. 38, the system may be further configured to indicate,via a suitable line or other visual, a transfer of data between a firstasset in a first location and a second asset in a second location. Asmay be understood from this figure, the transfer indicated by the linehas a “High” risk level, contains sensitive data that includes acustomer credit card, has a source location of Spain (e.g., at amerchant), and has a destination location of Brazil (e.g., in adatabase). In various other embodiments, the system may generate avisual representation that includes a plurality of transfers between aplurality of asset locations.

Adaptive Execution on a Data Model

In various embodiments, a Data Model Adaptive Execution System may beconfigured to take one or more suitable actions to remediate anidentified risk trigger in view of one or more regulations (e.g., one ormore legal regulations, one or more binding corporate rules, etc.). Forexample, in order to ensure compliance with one or more legal orindustry standards related to the collection and/or storage of privateinformation (e.g., personal data), an entity may be required to modifyone or more aspects of a way in which the entity collects, stores,and/or otherwise processes personal data (e.g., in response to a changein a legal or other requirement). In order to identify whether aparticular change or other risk trigger requires remediation, the systemmay be configured to assess a relevance of the risk posed by thepotential risk trigger and identify one or more processing activities ordata assets that may be affected by the risk.

Certain functionality of a Data Model Adaptive Execution System may beimplemented via an Adaptive Execution on a Data Model Module 3900. Aparticular embodiment of the Adaptive Execution on a Data Model Module3900 is shown in FIG. 39. When executing the Adaptive Execution on aData Model Module 3900, the system may be configured, at Step 3910, toidentify and/or detect one or more potential risk triggers. Inparticular embodiments, the system is configured to identify one or morepotential risk triggers in response to receiving a notification of asecurity breach (e.g., data breach) of one or more data assets (e.g.,one or more data assets utilized by a particular organization). Forexample, in response to receiving an indication that Salesforce (e.g., acustomer relationship management platform) has had a data breach, thesystem may identify one or more potential risk triggers in the form ofany data that the system receives from, or processes via Salesforce.

In still other embodiments, the system is configured to identify one ormore potential risk triggers in response to determining (e.g., receivingan input or indication) that one or more legal or industry requirementsthat relate to the collection, storage, and/or processing of personaldata have changed. For example, a particular legal regulation related toan amount of time that personal data can be stored, an encryption levelrequired to be applied to personal data, etc. may change. As anotherexample, a safe harbor arrangement (e.g., such as the safe harborarrangement discussed above) may be determined to be inadequatejustification for a transfer of data between a first and secondlocation. In this example, the system may be configured to receive anindication that ‘safe harbor’ is no longer an adequate justification fordata transfer from a first asset in a first location to a second assetin a second location.

Continuing to Step 3920, the system is configured to assess and analyzethe one or more potential risk triggers to determine a relevance of arisk posed by the one or more potential risk triggers. The system may,for example, determine whether the one or more potential risk triggersare related to one or more data assets (e.g., one or more data elementsof one or more data assets) and/or processing activities associated witha particular entity. When analyzing the one or more potential risktriggers to determine a relevance of a risk posed by the one or morepotential risk triggers, the system may be configured to utilize (e.g.,use) a formula to determine a risk level of the identified one or morepotential risk triggers. The system may, for example, determine the risklevel based at least in part on: (1) an amount of personal data affectedby the one or more potential risk triggers; (2) a type of personal dataaffected by the one or more potential risk triggers; (3) a number ofdata assets affected by the one or more potential risk triggers; and/or(4) any other suitable factor.

For example, in response to identifying a data breach in Salesforce, thesystem may, for example: (1) determine whether one or more systemsassociated with the entity utilize Salesforce; and (2) assess the one ormore systems utilized by Salesforce to evaluate a risk posed by the databreach. The system may, for example, determine that the entity utilizesSalesforce in order to store customer data such as name, address,contact information, etc. In this example, the system may determine thatthe Salesforce data breach poses a high risk because the data breach mayhave resulted in a breach of personal data of the entity's customers(e.g., data subjects).

In still another example, in response to determining that safe harbor isno longer a valid justification for a data transfer between twolocations, the system may be configured to: (1) determine whether one ormore data transfers involving one or more data assets associated withthe particular entity are currently justified via a safe harborarrangement; and (2) in response to determining that the one or moredata transfers are currently justified via a safe harbor arrangement,assessing a risk of the one or more transfers in view of the determinedinadequacy of safe harbor as a data transfer justification. Inparticular embodiments, the system may identify one or more supplementaljustifications and determine that the determined inadequacy of safeharbor poses a low risk. In other embodiments, the system may beconfigured to determine that the determined inadequacy of safe harborposes a high risk (e.g., because the system is currently performing oneor more data transfers that may be in violation of one or more legal,internal, or industry regulations related to data transfer).

Returning to Step 3930, the system is configured to use one or more datamodeling techniques to identify one or more processing activities and/ordata assets that may be affected by the risk. As discussed above, thesystem may utilize a particular data model that maps and/or indexes dataassociated with a particular data asset. The data model may, forexample, define one or more data transfers, one or more types of data,etc. that are associated with a particular data asset and/or processingactivity. In some embodiments, the system is configured to use the datamodel to identify one or more data assets and/or processing activitiesthat may be affected by the risk assessed at Step 3920. In variousembodiments, the system is configured to identify, using any suitabledata modeling technique described herein, one or more pieces of personaldata that the system is configured to collect, store, or otherwiseprocess that may be affected by the one or more potential risk triggers.

Next, at Step 3940, the system is configured to determine, based atleast in part on the identified one or more processing activities and/ordata assets and the relevance of the risk, whether to take one or moreactions in response to the one or more potential risk triggers. Inparticular embodiments, the system may, for example: (1) determine totake one or more actions in response to determining that a calculatedrisk level is above a threshold risk level; (2) determine to take theone or more actions in response to determining that the one or morepotential risk triggers may place the entity in violation of one or moreregulations (e.g., legal and/or industry regulations); etc.

In some embodiments, the system may determine whether to take one ormore actions based at least in part on input from one or moreindividuals associated with the entity. The one or more individuals mayinclude, for example, one or more privacy officers, one or more legalrepresentatives, etc. In particular embodiments, the system isconfigured to receive input from the one or more individuals, anddetermine whether to take one or more actions in response to the input.

Continuing to Step 3950, the system is configured to take one or moresuitable actions to remediate the risk in response to identifying and/ordetecting the one or more potential risk triggers.

In particular embodiments, the one or more actions may include, forexample: (1) adjusting one or more data attributes of a particular dataasset (e.g., an encryption level of data stored by the data asset, oneor more access permissions of data stored by the particular data asset,a source of data stored by the particular data asset, an amount of timethe data is stored by a particular asset, etc.); (2) generating a reportindicating the risk level and the identified one or more risk triggers;(3) providing the report to one or more individuals (e.g., a privacyofficer or other individual); and/or (4) taking any other suitableaction, which may, for example, be related to the identified one or morepotential risk triggers.

Automatic Risk Remediation Process

In various embodiments, a system may be configured to substantiallyautomatically determine whether to take one or more actions in responseto one or more identified risk triggers as discussed above in thecontext of the Adaptive Execution on a Data Model Module 3900. Inparticular embodiments, the system is configured to substantiallyautomatically perform one or more steps related to the analysis of andresponse to the one or more potential risk triggers discussed above. Forexample, the system may substantially automatically determine arelevance of a risk posed by (e.g., a risk level) the one or morepotential risk triggers based at least in part on one or morepreviously-determined responses to similar risk triggers. This mayinclude, for example, one or more previously determined responses forthe particular entity that has identified the current risk trigger, oneor more similarly situated entities, or any other suitable entity orpotential trigger.

In particular embodiments, the system may, for example, when determiningwhether to take one or more actions in response to the one or morepotential risk triggers (e.g., as discussed above with respect to Step3940 of the Adaptive Execution on a Data Model Module): (1) compare thepotential risk trigger to one or more previous risks triggersexperienced by the particular entity at a previous time; (2) identify asimilar previous risk trigger (e.g., one or more previous risk triggersrelated to a similar change in regulation, breach of data, type of issueidentified, etc.); (3) determine the relevance of the current risktrigger based at least in part on a determined relevance of the previousrisk trigger; and (4) determine whether to take one or more actions tothe current risk trigger based at least in part on one or moredetermined actions to take in response to the previous, similar risktrigger.

Similarly, in particular embodiments, the system may be configured tosubstantially automatically determine one or more actions to take inresponse to a current potential risk trigger based on one or moreactions taken by one or more similarly situated entities to one or moreprevious, similar risk triggers. For example, the system may beconfigured to: (1) compare the potential risk trigger to one or moreprevious risk triggers experienced by one or more similarly situatedentities at a previous time; (2) identify a similar previous risktrigger (e.g., one or more previous risk triggers related to a similarchange in regulation, breach of data, and/or type of issue identified,etc. from the one or more previous risk triggers experienced by the oneor more similarly-situated entities at the previous time; (3) determinethe relevance of the current risk trigger based at least in part on adetermined relevance of the previous risk trigger (e.g., a relevancedetermined by the one or more similarly situated entities); and (4)determine one or more actions to take in response to the current risktrigger based at least in part on one or more previously determinedactions to take in response to the previous, similar risk trigger (e.g.,one or more determined actions by the one or more similarly situatedentities at the previous time).

In various embodiments, the one or more similarly-situated entities mayinclude, for example: (1) one or more other entities in a geographiclocation similar to a geographic location of the entity that hasidentified the one or more potential risk triggers (e.g., a similarcountry, jurisdiction, physical location, etc.); (2) one or more otherentities in a similar industry (e.g., banking, manufacturing,electronics, etc.); (3); one or more entities of a similar size (e.g.,market capitalization, number of employees, etc.); (4) one or moreentities that are governed by one or more similar regulations (e.g.,such as any suitable regulation discussed herein); and/or (5) any othersuitably similarly situated entity.

In various embodiments, the system is configured to use one or moremachine learning techniques to analyze one or more risk levels assignedto previously identified risk triggers, determine a suitable response tosimilar, currently-identified risk triggers based on previouslydetermined responses, etc.

In particular embodiments, the system may, for example, be configuredto: (1) receive risk remediation data for a plurality of identified risktriggers from a plurality of different entities; (2) analyze the riskremediation data to determine a pattern in assigned risk levels anddetermined response to particular risk triggers; and (3) develop a modelbased on the risk remediation data for use in facilitating an automaticassessment of and/or response to future identified risk triggers.

In a particular example of a reactive system for automaticallydetermining a suitable action to take in response to an identified risktrigger, the system may take one or more suitable actions in response toidentifying a data beach in Salesforce (e.g., as discussed above). Inparticular embodiments, the system may, for example: (1) substantiallyautomatically identify one or more actions taken by the system inresponse to a similar data breach of one or more different vendors; and(2) determine a suitable action to take in response to the data breachbased on the one or more actions taken in response to the similar databreach. The similar data breach may include, for example, a breach indata of a similar type, or any other similar breach.

In another example, the system may be configured to identify one or moresimilarly situated entities that have experienced a data breach viaSalesforce or other similar vendor. The system, may, for example, beconfigured to determine a suitable action to take based at least in parton an action taken by such a similar entity to a similar data breach. Instill another example, the system may be configured, based on one ormore previous determinations related to a data breach by a vendor (e.g.,such as by Salesforce) to take no action in response to the identifiedrisk trigger (e.g., because the identified risk may pose no or minimaldanger).

Systems and Methods for Automatically Remediating Identified Risks

A data model generation and population system, according to particularembodiments, is configured to generate a data model (e.g., one or moredata models) that maps one or more relationships between and/or among aplurality of data assets utilized by a corporation or other entity(e.g., individual, organization, etc.) in the context, for example, ofone or more business processes. In particular embodiments, each of theplurality of data assets (e.g., data systems) may include, for example,any entity that collects, processes, contains, and/or transfers data(e.g., such as a software application, “internet of things” computerizeddevice, database, website, data-center, server, etc.). For example, afirst data asset may include any software or device (e.g., server orservers) utilized by a particular entity for such data collection,processing, transfer, storage, etc.

In particular embodiments, a system may be configured to generate andmaintain one or more disaster recovery plans for particular data assetsbased on one or more relationships between/among one or more data assetsoperated and/or utilized by a particular entity.

In various embodiments, a system may be configured to substantiallyautomatically determine whether to take one or more actions in responseto one or more identified risk triggers. For example, an identified risktrigger include any suitable risk trigger such as that a data asset foran organization is hosted in only one particular location therebyincreasing the scope of risk if the location were infiltrated (e.g., viacybercrime). In particular embodiments, the system is configured tosubstantially automatically perform one or more steps related to theanalysis of and response to the one or more potential risk triggersdiscussed above. For example, the system may substantially automaticallydetermine a relevance of a risk posed by (e.g., a risk level) the one ormore potential risk triggers based at least in part on one or morepreviously-determined responses to similar risk triggers. This mayinclude, for example, one or more previously determined responses forthe particular entity that has identified the current risk trigger, oneor more similarly situated entities, or any other suitable entity orpotential trigger.

In particular embodiments, the system may, for example, be configuredto: (1) receive risk remediation data for a plurality of identified risktriggers from a plurality of different entities; (2) analyze the riskremediation data to determine a pattern in assigned risk levels anddetermined response to particular risk triggers; and (3) develop a modelbased on the risk remediation data for use in facilitating an automaticassessment of and/or response to future identified risk triggers.

In some embodiments, in response to a change or update is made to one ormore processing activities and/or data assets (e.g., a databaseassociated with a particular organization), the system may use datamodeling techniques to update the risk remediation data for use infacilitating an automatic assessment of and/or response to futureidentified risk triggers. For example, the system may be configured touse a data map and/or data model described herein to, for example: (1)particular systems that may require some remedial action in response toan identified breach/incident for one or more related systems; (2)automatically generate a notification to an individual to update adisaster recovery plan for those systems; and/or (3) automaticallygenerate a disaster recovery plan that includes one or more actions inresponse to identifying an incident in one or more related systemsidentified using the data mapping techniques described herein. Invarious embodiments, in response to modification of a privacy campaign,processing activity, etc. of the particular organization (e.g., add,remove, or update particular information), the system may update therisk remediation data for use in facilitating an automatic assessment ofand/or response to future identified risk triggers. For example, thesystem may be configured to (1) identify one or more changes to one ormore relationships between/among particular data assets in response to achange in one or more business processes; and (2) modify (e.g., and/orgenerate a notification to modify) one or more disaster recovery plansfor any affected data assets.

In particular embodiments, the system may, for example, be configuredto: (1) access risk remediation data for an entity that identifies oneor more suitable actions to remediate a risk in response to identifyingone or more data assets of the entity that may be affected by one ormore potential risk triggers; (2) receive an indication of an update tothe one or more data assets; (3) identify one or more potential updatedrisk triggers for an entity; (4) assess and analyze the one or morepotential updated risk triggers to determine a relevance of a risk posedto the entity by the one or more potential updated risk triggers; (5)use one or more data modeling techniques to identify one or more dataassets associated with the entity that may be affected by the risk; and(6) update the risk remediation data to include the one or more actionsto remediate the risk in response to identifying the one or morepotential updated risk triggers.

Webform Crawling to Map Processing Activities in a Data Model

In particular embodiments, a data mapping system (e.g., such as anysuitable data mapping and/or modeling system described herein) may beconfigured to generate a data model that maps one or more relationshipsbetween and/or among a plurality of data assets utilized by acorporation or other entity (e.g., individual, organization, etc.) inthe context, for example, of one or more business processes and/orprocessing activities. In various embodiments, when generating the datamodel, the system may identify one or more webforms utilized by thesystem in the collection and processing of personal data and determineone or more particular data assets and/or processing activities thatutilize such data. Although in the course of this description, thesystem is described as crawling (e.g., and/or scanning) one or morewebforms, it should be understood that other embodiments may be utilizedto scan, crawl or analyze any suitable electronic form in order to mapany data input via the electronic form in any suitable manner.

In particular embodiments, the system may be configured to use one ormore website scanning tools to, for example: (1) identify a webform(e.g., on a website associated with a particular entity ororganization); (2) robotically complete the webform; (3) and analyze thecompleted webform to determine one or more particular processingactivities, and/or business processes, etc. that use one or more piecesof data submitted via the webform.

As may be understood in light of this disclosure, one or more legaland/or industry regulations may require an entity to, for example,maintain a record of one or more processing activities undertaken by theentity that includes: (1) a name and contact details of a controllerresponsible for the processing activity; (2) a purpose of theprocessing; (3) a description of one or more categories of data subjectsand/or of one or more categories of personal data collected as part ofthe processing activity; (4) one or more categories of recipients towhom the personal data may be disclosed, including recipients in one ormore second countries or other locations; (5) one or more transfers ofthe personal data to a second country or an international organization;(6) a time limit for erasure of the personal data, if applicable; (7) anidentification of one or more security measures taken in the collectionand/or storage of the personal data; and/or (8) any other suitableinformation.

As may be further understood in light of this disclosure, a particularorganization may undertake a plurality of different privacy campaigns,processing activities, etc. that involve the collection and storage ofpersonal data. In some embodiments, each of the plurality of differentprocessing activities may collect redundant data (e.g., may collect thesame personal data for a particular individual more than once), and maystore data and/or redundant data in one or more particular locations(e.g., on one or more different servers, in one or more differentdatabases, etc.). Additionally, one or more sub-organizations (e.g.,subgroups) of an organization or entity may initiate a processingactivity that involves the collection of personal data without vettingthe new processing activity with a privacy compliance officer or otherindividual within the company tasked with ensuring compliance with oneor more prevailing privacy regulations. In this way, a particularorganization may collect and store personal data in a plurality ofdifferent locations which may include one or more known and/or unknownlocations, or may collect personal data for a purpose that is notimmediately apparent (e.g., using one or more webforms). As such, it maybe desirable for an entity to implement a system that is configured toscan one or more webforms that collect personal data to identify whichparticular processing activity (e.g., or processing activities) thatpersonal data is utilized in the context of.

Various processes are performed by the Data Access Webform CrawlingSystem and may be implemented by a Webform Crawling Module 4300.Referring to FIG. 43, in particular embodiments, the system, whenexecuting the Webform Crawling Module 4300, is configured to: (1)identify a webform used to collect one or more pieces of personal data;(2) robotically complete the identified webform; (3) analyze thecompleted webform to determine one or more processing activities thatutilize the one or more pieces of personal data collected by thewebform; (4) identify a first data asset in the data model that isassociated with the one or more processing activities; (5) modify a datainventory for the first data asset in the data model to include dataassociated with the webform; and (6) modify the data model to includethe modified data inventory for the first data asset.

When executing the Webform Crawling Module 4300, the system begins, atStep 4310, by identifying a webform used to collect one or more piecesof personal data. The system may use one or more website scanning toolsto identify the webform. The webform may be a website associated with aparticular entity or organization. For example, the webform may be a“Contact Us” form that is on the particular organization's website orany other type of webform associated with the particular organization.At Step 4320, the system is configured to robotically complete theidentified webform. The identified webform may be completed by using avirtual profile that emulates a user profile, and the virtual profilemay include an e-mail address. The system may monitor the e-mail accountassociated with the e-mail address for a confirmation e-mail related tothe completion of the identified webform where the system may receiveand interact with the confirmation e-mail. Additionally, the system mayanalyze (e.g., scrape) the confirmation e-mail for the data associatedwith the webform. The data associated with the webform may identify oneor more processing activities and one or more pieces of personal datacollected by the webform.

Next, at Step 4330, the system is configured to analyze the completedwebform to determine one or more processing activities that utilize theone or more pieces of personal data collected by the webform. In someimplementations, the system may analyze one or more pieces of computercode associated with the webform to determine the one or more processingactivities that utilize the one or more pieces of personal datacollected by the webform. Further, the system may analyze the one ormore pieces of computer code to identify a storage location to which theone or more pieces of personal data collected by the webform are routed.At Step 4340, the system is configured to identify a first data asset inthe data model that is associated with the one or more processingactivities. In some implementations, the system may identify aprocessing activity based on the storage location of the identified oneor more pieces of personal data, and an asset may be associated with aparticular storage location.

Continuing to Step 4350, the system is configured to modify a datainventory for the first data asset in the data model to include dataassociated with the webform. The system may include an indication thatthe one or more processing activities operate with data included in thefirst data asset. Additionally, the system may indicate that the one ormore pieces of personal data are utilized by the identified one or moreprocessing activities.

At Step 4360, the system continues by modifying the data model toinclude the modified data inventory for the first data asset. In someimplementations, the system may include a mapping of the first dataasset to the one or more processing activities that utilize the one morepieces of personal data. The mapping may be based on the analysis of thecomputer code associated with the webform. Moreover, in someimplementations, the system may add the first data asset to athird-party data repository, and the first data asset may include anelectronic link to the webform. The third-party repository is furtherdiscussed below.

Central Consent Repository

In particular embodiments, any entity (e.g., organization, company,etc.) that collects, stores, processes, etc. personal data may requireone or more of: (1) consent from a data subject from whom the personaldata is collected and/or processed; and/or (2) a lawful basis for thecollection and/or processing of the personal data. In variousembodiments, the entity may be required to, for example, demonstratethat a data subject has freely given specific, informed, and unambiguousindication of the data subject's agreement to the processing of his orher personal data for one or more specific purposes (e.g., in the formof a statement or clear affirmative action). As such, in particularembodiments, an organization may be required to demonstrate a lawfulbasis for each piece of personal data that the organization hascollected, processed, and/or stored. In particular, each piece ofpersonal data that an organization or entity has a lawful basis tocollect and process may be tied to a particular processing activityundertaken by the organization or entity.

A particular organization may undertake a plurality of different privacycampaigns, processing activities, etc. that involve the collection andstorage of personal data. In some embodiments, each of the plurality ofdifferent processing activities may collect redundant data (e.g., maycollect the same personal data for a particular individual more thanonce), and may store data and/or redundant data in one or moreparticular locations (e.g., on one or more different servers, in one ormore different databases, etc.). In this way, because of the number ofprocessing activities that an organization may undertake, and the amountof data collected as part of those processing activities over time, oneor more data systems associated with an entity or organization may storeor continue to store data that is not associated with any particularprocessing activity (e.g., any particular current processing activity).Under various legal and industry standards related to the collection andstorage of personal data, such data may not have or may no longer have alegal basis for the organization or entity to continue to store thedata. As such, organizations and entities may require improved systemsand methods to maintain an inventory of data assets utilized to processand/or store personal data for which a data subject has provided consentfor such storage and/or processing.

In various embodiments, the system is configured to provide athird-party data repository system to facilitate the receipt andcentralized storage of personal data for each of a plurality ofrespective data subjects, as described herein. Additionally, thethird-party data repository system is configured to interface with acentralized consent receipt management system.

In various embodiments, the system may be configured to, for example:(1) identify a webform used to collect one or more pieces of personaldata, (2) determine a data asset of a plurality of data assets of theorganization where input data of the webform is transmitted, (3) add thedata asset to the third-party data repository with an electronic link tothe webform, (4) in response to a user submitting the webform, create aunique subject identifier to submit to the third-party data repositoryand the data asset along with the form data provided by the user in thewebform, (5) submit the unique subject identifier and the form dataprovided by the user in the webform to the third-party data repositoryand the data asset, and (6) digitally store the unique subjectidentifier and the form data provided by the user in the webform in thethird-party data repository and the data asset.

In some embodiments, the system may be further configured to, forexample: (1) receive a data subject access request from the user (e.g.,a data subject rights' request, a data subject deletion request, etc.),(2) access the third-party data repository to identify the uniquesubject identifier of the user, (3) determine which data assets of theplurality of data assets of the organization include the unique subjectidentifier, (4) access personal data of the user stored in each of thedata assets of the plurality of data assets of the organization thatinclude the unique subject identifier, and (5) take one or more actionsbased on the data subject access request (e.g., delete the accessedpersonal data for a data subject deletion request).

The system may, for example: (1) generate, for each of a plurality ofdata subjects, a respective unique subject identifier in response tosubmission, by each data subject, of a particular webform; (2) maintaina database of each respective unique subject identifier; and (3)electronically link each respective unique subject identifier to eachof: (A) a webform initially submitted by the user; and (B) one or moredata assets that utilize data received from the data subject via thewebform.

The Webform Crawling Data System may also implement a Data Asset andWebform Management Module 4400. Referring to FIG. 44, in particularembodiments, the system, when executing the Data Asset and WebformManagement Module 4400, is configured for: (1) identifying a webformused to collect one or more pieces of personal data; (2) determining adata asset of a plurality of data assets of the organization where inputdata of the webform is transmitted; (3) adding the data asset to thethird-party data repository with an electronic link to the webform; (4)in response to a user submitting the webform, creating a unique subjectidentifier to submit to the third-party data repository and the dataasset along with form data provided by the user in the webform; (5)submitting the unique subject identifier and the form data provided bythe user in the webform to the third-party data repository and the dataasset; and (6) digitally storing the unique subject identifier and theform data provided by the user in the webform in the third-party datarepository and the data asset.

When executing the Data Asset and Webform Management Module 4400, thesystem begins, at Step 4410, by identifying a webform used to collectone or more pieces of personal data. In particular embodiments, thesystem may be configured to use one or more website scanning tools to,for example, identify a webform. The webform may be a website associatedwith a particular entity or organization. For example, the webform maybe a “Contact Us” form that is on the particular organization's websiteor any other type of webform associated with the particularorganization.

At Step 4420, the system is configured to determine a data asset of aplurality of data assets of the organization where input data of thewebform is transmitted. The system may perform the determination byidentifying where the input data of the webform is transmitted (e.g.,Salesforce). Continuing to Step 4430, the system is configured to addthe data asset to the third-party data repository with an electroniclink to the webform. The system may provide the third-party datarepository with a reference to the data asset, or in someimplementations, the system may provide the one or more pieces ofpersonal data that were transmitted to the one or more data assets tothe third-party repository. The system may associate the electronic linkto the webform with the identified data asset that includes the one ormore pieces of personal data.

Returning to Step 4440, the system is configured to create a uniquesubject identifier to submit to the third-party data repository and thedata asset along with form data provided by the user in the webform inresponse to a user submitting the webform. In response to a userinputting form data (e.g., name, address, credit card information, etc.)at the webform and submitting the webform, the system may, based on thelink to the webform, create a unique subject identifier to identify theuser. The unique subject identifier may be any type of numerical,alphabetical, or any other type of identifier to identify the user.

Continuing to Step 4450, the system is configured to submit the uniquesubject identifier and the form data provided by the user in the webformto the third-party data repository and the data asset. The system isconfigured to submit the unique subject identifier to the third-partydata repository and the data asset along with the form data. Further,the system may use the unique subject identifier of a user to access andupdate each of the data assets of the particular organization (i.e.,including the other data assets of the particular organization where theform data is not transmitted). For example, in response to a usersubmitting a data subject access request to delete personal data theparticular organization has stored of the user, the system may use theunique subject identifier of the user to access and retrieve the user'spersonal data stored in all of the data assets (e.g., Salesforce,Eloqua, Marketo, etc.) utilized by the particular organization. At Step4460, the system continues by digitally storing the unique subjectidentifier and the form data provided by the user in the webform in thethird-party data repository and the data asset.

Further, in some implementations, the system may be configured toreceive a data subject access request from the user. The data subjectaccess request may be one or more different types of data subject accessrequests, and may be, for example, a data subject deletion request or adata subject rights request. Upon the system receiving the data subjectaccess request, the system may be configured to access the third-partydata repository to identify the unique subject identifier of the user,determine which data assets of the plurality of data assets of theorganization include the unique subject identifier, and access personaldata of the user stored in each of the data assets of the plurality ofdata assets of the organization that include the unique subjectidentifier. Upon the data subject access request being a data subjectdeletion request, then the system may delete the accessed personal dataof the user stored in each of the data assets of the plurality of dataassets of the organization that include the unique subject identifier.When the data subject access request is a data subject rights request,the system may generate a data subject rights request report thatincludes the accessed personal data of the user stored in each of thedata assets of the plurality of data assets of the organization thatinclude the unique subject identifier. Further, the data subject rightsrequest report may be transmitted to the user. In some implementations,the system may transmit the data subject rights request report to theuser via a secure electronic link.

Webform Generation User Experience

FIG. 40 depicts an exemplary webform that a particular entity mayinclude on a website for completion by one or more customers or users ofthe website. As may be understood from FIG. 40, the webform may collectpersonal data such as, for example: (1) first name; (2) last name; (3)organization name; (4) country of residence; (5) state; (6) phonenumber; (7) e-mail address; (8) website; and/or (9) any other suitablepersonal data. As may be further understood from this Figure, an entity(e.g., or a system controlled by the entity) may use the webform tocollect such personal data as part of one or more processing activities(e.g., e-mail marketing, online surveys, event marketing, etc.). Invarious embodiments, the system may be configured to scan a particularwebform to identify a particular processing activity for which theentity is collecting the personal data.

In various embodiments, the system may, for example: (1) roboticallyfill out the webform (e.g., using one or more virtual profiles); (2)analyze one or more pieces of computer code associated with the webform(e.g., javascript, HTML, etc.); and (3) map one or more businessprocesses that utilize the data collected via the webform based at leastin part on the analyzed one or more pieces of computer code. Inparticular embodiments, a particular entity that utilizes a webform tocollect personal data for use in a particular processing activity (e.g.,business process) may analyze one or more pieces of computer codeassociated with the webform to determine: (1) one or more systemsassociated with the entity to which data entered the webform is routed(e.g., one or more data assets that serve as a destination asset to dataentered via the webform); (2) a purpose for the collection of the dataentered via the webform (e.g., a processing activity that utilizes thedestination asset discussed above; (3) a type of data collected via thewebform; and/or (4) any other suitable information related to thecollection of data via the webform.

In particular embodiments, a system may be configured to transmit awebform completion confirmation e-mail to a user that completes thewebform. In various embodiments, the system may be configured to analyzethe e-mail or other message to identify one or more business processesthat utilize the data collected by the webform (e.g., byanalyzing/scraping one or more contents of the e-mail or other message).The system may then determine a purpose of the data collection and/or anassociated processing activity based at least in part on the analysis.

Scanning Electronic Correspondence to Facilitate Automatic Data SubjectAccess Request Submission

In various embodiments, any system described herein may be configuredfor: (1) analyzing electronic correspondence associated with a datasubject (e.g., the emails within one or more email in-boxes associatedwith the data subject, or a plurality of text messages); (2) based onthe analysis, identifying one or more entities (e.g., corporateentities) that that the data subject does not actively do business with(e.g., as evidenced by the fact that the data subject no longer opensemails from the entity, has set up a rule to automatically delete emailsreceived from the entity, has blocked texts from the entity, etc.); (3)in response to identifying the entity as an entity that the data subjectno longer does business with, at least substantially automaticallygenerating a data subject access request and, optionally, automaticallysubmitting the data subject access request to the identified entity.

The system may, for example, be configured to determine whether the datasubject still uses one or more services from a particular e-mail sender(e.g., service provider) based at least in part on one more determinedinteractions of the data subject with one or more e-mails, or otherelectronic correspondence, from the service provider (e.g., whether thedata subject reads the e-mail, selects one or more links within thee-mail, deletes the e-mail without reading it, etc.). The system maythen substantially automatically generate and/or complete a data subjectaccess request on behalf of the data subject that includes a request tobe forgotten (e.g., a request for the entity to delete some or all ofthe data subject's personal data that the entity is processing).

For purposes of simplicity, various embodiments will now be described inwhich the system scans a plurality of emails associated with a datasubject in order to identify one or more entities that the data subjectno longer does business with. However, it should be understood that, inother embodiments, the same or similar techniques may be used inanalyzing other types of electronic or other correspondence to identifyentities that the data subject no longer does business with. Forexample, the system may analyze text messages, social media posts, scansof paper mail, or any other correspondence and/or other documentsassociated with the data subject to determine whether the data subjectdoes business with particular entities. In various embodiments, thesystem bases this determination on its analysis of multiple differenttypes of electronic correspondence between the data subject and one ormore entities (which may include one-way correspondence in which therecipient of a particular correspondence doesn't respond, or two-waycorrespondence, in which the recipient of the correspondence responds tothe correspondence).

In various embodiments, various functions performed by an E-mailScanning System may be implemented via an E-mail Scanning Module 4100.FIG. 41 depicts an E-mail Scanning Module 4100 according to a particularembodiment, which may be executed, for example, on any of the servers110, 120, 130, 160 shown in FIG. 1, or on one or more remote computingdevices 150. When executing an exemplary E-mail Scanning Module 4100,the system begins, at Step 4110, by providing a software application forinstallation on a computing device. In particular embodiments, thesoftware application may be configured to integrate with an e-mailservice (e.g., gmail, yahoo, live, Microsoft Exchange, etc.) in order toprovide access to a data subject's e-mail (e.g., a data subject'se-mail). In particular embodiments, the software application may beembodied as a software plugin that interfaces with a particular softwareapplication (e.g., Microsoft Outlook) in order to provide access to thedata subject's e-mail to the systems. In other embodiments, the softwareapplication may be embodied as a browser plugin for use with a webbrowser to provide access to the data subject's web-based e-mailservice. In particular embodiments, the system is configured to providethe software application for installation on a data subject's computingdevice (e.g., mobile computing device, etc.). In such embodiments, thesoftware application may be embodied as a client-side softwareapplication that executes one or more of the processes described belowon a client computing device (e.g., such as the data subject's computingdevice on which the data subject accesses his or her e-mails).

In still other embodiments, the system is configured to provide thesoftware application for installation on one or more suitable servers(e.g., one or more suitable servers that host a particular e-mailservice). In particular embodiments, for example, the system isconfigured to: (1) receive authorization from a data subject to accesshis or her e-mails; and (2) use a software application installed on oneor more remote servers to perform one or more of the functions describedbelow. In such embodiments, the system may be configured to provide thesoftware application to the one or more remote servers. In particularother embodiments, the system may be at least partially integrated inone or more remote servers (e.g., via a direct server integration). Insuch embodiments, the system may be at least partially integrated withone or more remote e-mail servers (e.g., one or more remote servers thatstore and/or process a data subject's emails).

Returning to Step 4120, the system is configured to use the softwareapplication to scan and optionally index one or more data subjecte-mails, and then analyze information derived from the emails toidentify a subject entity (e.g., corporate or non-corporate entity) fromwhich each of the one or more data subject e-mails was received by adata subject. The system may, for example, be configured to scan and/orindex the data subject's emails to identify one or more subject entitiesas the sender of the emails. In particular embodiments, the one or moresubject entities may include one or more subject entities (e.g.,corporate entities) that would be required to respond to a data subjectaccess request, if received from the data subject. For example, the oneor more subject entities may include any subject company that collects,stores, or otherwise processes the data subject's personal data. Thesystem may, for example, be configured to identify particular e-mails ofthe data subject's indexed e-mails that were received from any suitableentity (e.g., Target, Home Depot, etc.). The system may, for example,scan an e-mail's subject field, body, sender, etc. to identify, forexample: (1) a name of the subject company; (2) an e-mail domainassociated with the subject company; and/or (3) any other suitableinformation which may identify the subject entity as the sender of thee-mail.

In some embodiments, the system may be configured to identify e-mailmessages from a subject entity based at least in part on an emailmailbox in which the messages are located in the data subject's e-mailaccount. For example, the data subject's e-mail account may alreadypre-sort incoming messages into one or more categories (e.g., which mayinclude, for example, a promotions category, a junk category, etc.). Insuch embodiments, the system may be configured to limit the one or moree-mails that the system scans and/or indexes to e-mails that have beenidentified as promotional in nature (or that have been placed into anyother pre-defined category, such as Spam) by the data subject's e-mailservice.

Continuing to Step 4130, the system is configured to use an algorithm todetermine whether the data subject actively does business with theentity. In particular embodiments, the system is configured to make thisdetermination based at least in part on (e.g., partially or entirelyon): (1) whether the data subject opens any of the one or more e-mailsreceived from the subject company; (2) how long the data subject spendsreviewing one or more of the e-mails that the data subject does openfrom the subject company; (3) whether the data subject deletes one ormore of the e-mails from the subject company without reading them; (4)what portion (e.g., percentage) of e-mails received from the subjectcompany the data subject opens; (5) whether the data subject selects oneor more links contained in one or more e-mails received from the subjectcompany; (6) how much time the data subject spends viewing a website towhich a link is provided in the one or more e-mails from the subjectcompany; (7) whether the data subject has set up a rule (e.g., asoftware-based rule) to auto-delete or block emails from the subjectcompany; (8) whether the data subject has set up a rule (e.g., asoftware-based rule) to redirect emails received from the subjectcompany to a specific folder or other location (e.g., a folderdesignated for commercial correspondence, or a folder designated forunwanted correspondence); (9) whether the data subject has submitted arequest to the particular entity for the particular entity not to sendemails to the data subject; (10) whether the data subject has submitteda request to the particular entity for the particular entity not to sendtext messages to the data subject; (11) whether the data subject hassubmitted a request to the particular entity for the particular entitynot to call the data subject; and/or (12) any other suitable informationrelated to the data subject's use of one or more services, or purchaseof goods, related to the one or more e-mails or other electroniccorrespondence received by the data subject from the subject company. Inparticular embodiments, the system is configured to automatically (e.g.,using one or more computer processors) determine the information of anyof the items listed above (e.g., whether the data subject has set up arule to redirect emails received from the subject company to a specificfolder) using any suitable technique.

As noted above, the system may, in addition, or alternatively, make thedetermination described above by analyzing electronic correspondenceother than emails, such as texts, social media postings, etc. thatinvolve the data subject and the entity. For example, the system maydetermine that the data subject no longer actively does business with aparticular entity if the data subject configures software (e.g.,messaging software on the data subject's smartphone) to block texts fromthe particular entity.

In various embodiments, the system is configured to utilize an algorithmthat takes into account one or more of the various factors discussedabove to determine whether the data subject still actively does businesswith the subject entity (e.g., and therefore would likely be interestedin continuing to receive e-mails from the subject company). In doing so,the system may assign any appropriate value to each of the factors indetermining whether to determine that the data subject no longer doesbusiness with the subject entity. Similarly, the system may allow thecalculation to be customized by allowing users to assign weightingfactors to each particular variable.

As a simple example, the system may use the following formula todetermine whether the data subject does business with a particularentity:

Data Subject Disengagement Rating=(Emails Opened Value)+(Texts ReadValue)+(Emails Automatically Deleted Value)+(Texts Blocked Value)

In a particular example, the system is configured to determine that thedata subject no longer actively does business with the entity if theData Subject Disengagement Rating is above 80. In this example, thesystem may assign: (1) a value of 80 to the Emails Read Value if thedata subject opens fewer than 5% of emails received from the from theentity; (2) a value of 50 to the Emails Read Value if the data subjectopens between 5%-25% of emails received from the entity; and (3) a valueof 0 to the Emails Read Value if the data subject opens over 25% ofemails received from the from the entity. The system may assign similarvalues to the other variables based on the user's other email and textrelated activities. For example, the system may assign a value of 100 toText Blocked Value if the data subject has actively blocked (e.g., viasoftware instructions) texts from the entity, and a value of 0 to TextBlocked Value if the data subject has not actively blocked texts fromthe entity. Similarly, the system may assign a value of 100 to EmailsAutomatically Deleted Value if the data subject has set software toautomatically delete (e.g., immediately delete or route to a junkfolder) emails from the entity, and a value of 0 to Emails AutomaticallyDeleted Value if the data subject has not initiated such a setting.

As noted above, the system may allow users to customize the calculationabove by assigning a weighting value to any of the values included inthe Data Subject Disengagement Rating calculation. For example, thesystem may allow the user to assign a weighting value of 1.2 to EmailsOpened Value if that particular user believes that this factor should beweighted 20% higher than usual in the calculation.

In various embodiments, the system is configured to, in response todetermining that the data subject no longer actively does business withthe entity, automatically generate, populate, and/or submit a datasubject access request to the entity. In various embodiments, the datasubject access request may include: (1) a request to delete some or allof the data subject's personal data that is being processed by theentity (e.g., in the form of a “right to be forgotten” request); (2) arequest to rectify inaccurate personal data of the data subject that isbeing processed by the entity; (3) a request to access of a copy ofpersonal information of the data subject processed by the entity; (4) arequest to restrict the processing of the data subject's data by theentity; and/or (5) a request to transfer the data subject's data fromthe entity to a specified controller.

As a particular example, the system may generate a focused request tohave the entity delete all of the data subject's personal data that theentity is processing in conjunction with a particular service offered bythe entity. For example, at Step 4140, the system is configured tosubstantially automatically complete one or more data subject accessrequests on behalf of the data subject for one or more services that thedata subject no longer uses.

FIG. 42 depicts an exemplary data subject access request form that thesystem may substantially automatically generate, complete and/or submitfor the data subject on the data subject's behalf. As shown in thisfigure, the system may complete information such as, for example: (1)what type of requestor the data subject is (e.g., employee, customer,etc.); (2) what the request involves (e.g., deleting data, etc.); (3)the requestor's first name; (4) the requestor's last name; (5) therequestor's email address; (6) the requestor's telephone number; (7) therequestor's home address; and/or (8) one or more details associated withthe request. In particular embodiments, the system is configured to usean index of information about a particular entity or service to automatefilling out the data subject access request.

In various embodiments, the system may receive at least some data fromthe data subject in order to complete the data subject access request.In other embodiments, the system is configured to scan one or moree-mails from the subject company to obtain one or more particular piecesof information for use in filling out the data subject access request(e.g., by identifying a shipping address in a particular e-mail, billingaddress, first name, last name, and/or phone number of the data subjectfrom a previous order that the data subject placed with the subjectcompany, etc.). In particular embodiments, the system may automaticallyidentify all of the information needed to populate the data subjectaccess request by identifying the information from within one or moreindividual electronic correspondence associated with the data subject(e.g., one or more texts or emails from the entity to the data subject).

In particular embodiments, the system may be configured to send amessage to the data subject (e.g., via e-mail) prior to automaticallycompleting the data subject access request. The message may, forexample, require the data subject to confirm that the data subject wouldlike the system to complete the request on the data subject's behalf. Invarious embodiments, in response to the data subject confirming that thedata subject would like the system to complete the request, the systemautomatically populates the request and submits the request to theentity on the data subject's behalf.

In other embodiments, the system may automatically submit the requestwithout explicit authorization from the data subject (e.g., the datasubject may have provided a blanket authorization for submitting suchrequests when configuring the system's settings.)

In some embodiments, the Email Scanning System may comprise a thirdparty system that is independent from the one or more subject entities.In such embodiments, the Email Scanning System may be implemented aspart of a service for data subjects who may desire to exercise one ormore privacy rights, but who aren't necessarily aware of which companiesmay be storing or processing their personal data, or who don't want tospend the time to submit data subject access requests manually.Similarly, various embodiments of the system may be implemented as partof a service that advantageously provides a data subject with anautomated way of submitting data subject access requests to subjectcompanies whose services the data subject no longer uses.

In still other embodiments, the system may be provided by a subjectentity (e.g., company) for use by data subjects. Because subjectcompanies are subject to requirements (e.g., in the form of laws andregulations) related to the storage and processing of personal data, itmay benefit the subject company to no longer burden itself with storingor processing data related to a data subject that is no longerpurchasing the subject entity's goods or utilizing the subject entity'sservices (e.g., that is no longer actively engaged with the entity). Insuch embodiments, the system may be configured to: (1) substantiallyautomatically submit the data subject access request; and (2) respond toand fulfill the data subject access request (e.g., the same system orrelated systems utilized by a particular subject entity may beconfigured to both submit and fulfill the data subject access request).In other embodiments, the subject entity may unilaterally modify (e.g.,edit or delete) the data subject's personal data within one or more ofits systems in response to determining that the data subject does notactively do business with the subject entity.

In particular embodiments for example, in response to the systemsubmitting a request to delete the data subject's personal data from asubject entity's systems, the system may: (1) automatically determinewhere the data subject's personal data, which is processed by thesubject entity, is stored; and (2) in response to determining thelocation of the data (e.g., which may be on multiple computing systems),automatically facilitate the deletion of the data subject's personaldata from the various systems (e.g., by automatically assigning one ormore tasks to delete data across one or more computer systems toeffectively delete the data subject's personal data from the systems).In particular embodiments, the step of facilitating the deletion of thepersonal data may comprise, for example: (1) overwriting the data inmemory; (2) marking the data for overwrite; (2) marking the data as free(e.g., and deleting a directory entry associated with the data); and/or(3) any other suitable technique for deleting the personal data. Inparticular embodiments, as part of this process, the system uses anappropriate data model (see discussion above) to efficiently determinewhere all of the data subject's personal data is stored.

Conclusion

Although embodiments above are described in reference to various privacymanagement systems, it should be understood that various aspects of thesystem described above may be applicable to other privacy-relatedsystems, or to other types of systems, in general.

Also, although various embodiments are described as having the systemanalyze a data subject's interaction with email, text messages (e.g.,SMS or MMS messages), or other electronic correspondence to determinewhether the data subject actively does business with a particularentity, in other embodiments, the system may make this determinationwithout analyzing electronic correspondence (e.g., emails or texts) or adata subject's interaction with electronic correspondence. For example,in particular embodiments, the system may automatically determinewhether a data subject has requested that a particular entity not sendemails to the data subject and, at least partially in response to makingthis determination, automatically generate, populate, and/or submit adata subject access request to the particular entity. Such a datasubject access request may include, for example, any of the various datasubject access requests described above (e.g., a request to delete allof the data subject's personal data that is being processed by theparticular entity). The system may execute similar functionality inresponse to determining that the data subject has requested that theparticular entity not send text (e.g., SMS or MMS) messages to the datasubject, call the data subject, etc.

It should be understood that, in various embodiments, the system maygenerate, populate, and/or submit any of the data subject accessrequests referenced above electronically (e.g., via a suitable computingnetwork).

While this specification contains many specific embodiment details,these should not be construed as limitations on the scope of anyinvention or of what may be claimed, but rather as descriptions offeatures that may be specific to particular embodiments of particularinventions. Certain features that are described in this specification inthe context of separate embodiments may also be implemented incombination in a single embodiment. Conversely, various features thatare described in the context of a single embodiment may also beimplemented in multiple embodiments separately or in any suitablesub-combination. Moreover, although features may be described above asacting in certain combinations and even initially claimed as such, oneor more features from a claimed combination may in some cases be excisedfrom the combination, and the claimed combination may be directed to asub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems maygenerally be integrated together in a single software product orpackaged into multiple software products.

Many modifications and other embodiments of the invention will come tomind to one skilled in the art to which this invention pertains havingthe benefit of the teachings presented in the foregoing descriptions andthe associated drawings. Therefore, it is to be understood that theinvention is not to be limited to the specific embodiments disclosed andthat modifications and other embodiments are intended to be includedwithin the scope of the appended claims. Although specific terms areemployed herein, they are used in a generic and descriptive sense onlyand not for the purposes of limitation.

What is claimed is:
 1. A computer-implemented data processing method ofidentifying and responding to one or more potential risk triggers basedon a data model, the method comprising: identifying, by one or moreprocessors, a potential risk trigger for an entity; assessing andanalyzing, by one or more processors, the potential risk trigger todetermine a relevance of a risk posed to the entity by the potentialrisk trigger; identifying, by one or more processors, using one or moredata modeling techniques, one or more data assets associated with theentity that may be affected by the potential risk trigger, whereinidentifying the one or more data assets comprises: accessing the datamodel to identify one or more pieces of personal data stored, collected,or processed by the one or more data assets; and analyzing the one ormore pieces of personal data to determine whether any of the one or morepieces of personal data may be affected by the potential risk trigger;determining, by one or more processors, based at least in part on theone or more identified data assets and the relevance of the risk,whether to take one or more actions in response to a potential riskposed to the entity by the potential risk trigger; and in response todetermining to take the one or more actions, taking, by one or moreprocessors, the one or more actions.
 2. The computer-implemented dataprocessing method of claim 1, wherein the potential risk triggercomprises a change in a regulation related to collection and storage ofpersonal data by the entity using the one or more identified dataassets.
 3. The computer-implemented data processing method of claim 1,the method further comprising: receiving an indication of a securitybreach of the one or more identified data assets; and in response toreceiving the indication, identifying the potential risk trigger as thesecurity breach.
 4. The computer-implemented data processing method ofclaim 1, wherein the method further comprises using the data model toidentify one or more data elements stored in the one or more dataassets, the data model comprising: a respective digital inventory foreach of the one or more data assets, each respective digital inventorycomprising one or more inventory attributes selected from the groupconsisting of: one or more processing activities associated with eachrespective data asset; transfer data associated with each respectivedata asset; one or more pieces of personal data associated with eachrespective data asset; and a data map identifying one or more electronicassociations between at least two of the one or more data assets.
 5. Thecomputer-implemented data processing method of claim 4, wherein the oneor more data elements comprise the one or more inventory attributes. 6.The computer-implemented data processing method of claim 5, wherein: thepotential risk trigger comprises a change in a regulation related tocollection and storage of personal data by the entity using the one ormore identified data assets; and determining whether to take the one ormore actions in response to the potential risk posed to the entity bythe potential risk trigger comprises analyzing the transfer dataassociated with each respective data asset.
 7. The computer-implementeddata processing method of claim 6, wherein the regulation comprises oneor more transfer restrictions.
 8. The computer-implemented dataprocessing method of claim 7, wherein the method further comprisesanalyzing the identified one or more data elements to determine one ormore data transfers between the one or more data systems in differentparticular physical locations.
 9. The computer-implemented dataprocessing method of claim 8, wherein the one or more data transferscomprise a first transfer from a first data asset in a first location toa second data asset in a second location; the one or more inventoryattributes associated with the first data asset comprise one or morefirst data storage attributes; and the one or more inventory attributesassociated with the second data asset comprise one or more second datastorage attributes.
 10. The computer-implemented data processing methodof claim 9, wherein: the one or more transfer restrictions comprise afirst transfer restriction related to the first transfer; and the firsttransfer restriction comprises a restriction that the one or more seconddata storage attributes comprise one or more second data securityrestrictions that are at least as stringent as one or more first datasecurity restrictions associated with the one or more first data storageattributes.
 11. A computer-implemented data processing method ofidentifying and responding to one or more potential risk triggers basedon a data model, the method comprising: identifying one or morepotential risk triggers for an entity; assessing and analyzing the oneor more potential risk triggers to determine a relevance of a risk posedto the entity by the one or more potential risk triggers; using one ormore data modeling techniques to identify one or more data assetsassociated with the entity that may be affected by the risk by: scanninga respective digital inventory for each of the one or more data assets,each respective digital inventory comprising one or more inventoryattributes selected from the group consisting of: one or more processingactivities associated with each respective data asset; transfer dataassociated with each respective data asset; and one or more pieces ofpersonal data associated with each respective data asset; and analyzingeach respective digital inventory to determine one or more inventoryattributes that may be affected by the risk; determining, based at leastin part on the one or more identified data assets and the relevance ofthe risk, whether to take one or more actions in response to the one ormore potential risk triggers; and taking a suitable action to remediatethe risk in response to identifying the one or more potential risktriggers.
 12. The computer-implemented data processing method of claim11, wherein the one or more potential risk triggers comprise a change toone or more regulations related to the storage of personal data by theone or more data assets.
 13. The computer-implemented data processingmethod of claim 11, wherein the one or more potential risk triggerscomprise a data breach associated with the one or more data assets. 14.The computer-implemented data processing method of claim 11, whereindetermining the relevance of the risk comprises calculating a risk levelof the one or more potential risk triggers based at least in part on oneor more risk factors.
 15. The computer-implemented data processingmethod of claim 11 wherein assessing and analyzing the one or morepotential risk triggers to determine the relevance of the risk posed tothe entity by the one or more potential risk triggers comprisesdetermining the relevance of the risk based at least in part on a typeof the one or more potential risk triggers.
 16. The computer-implementeddata processing method of claim 11, wherein the suitable actioncomprises modifying at least one of the one or more data assets.
 17. Thecomputer-implemented data processing method of claim 11 whereinassessing and analyzing the one or more potential risk triggers todetermine the relevance of the risk posed to the entity by the one ormore potential risk triggers comprises analyzing each respective digitalinventory to determine an amount of the one or more pieces of personaldata affected by the one or more potential risk triggers.
 18. Thecomputer-implemented data processing method of claim 11 whereinassessing and analyzing the one or more potential risk triggers todetermine the relevance of the risk posed to the entity by the one ormore potential risk triggers comprises analyzing each respective digitalinventory to determine a type of the one or more pieces of personal dataaffected by the one or more potential risk triggers.
 19. Thecomputer-implemented data processing method of claim 11 whereinassessing and analyzing the one or more potential risk triggers todetermine the relevance of the risk posed to the entity by the one ormore potential risk triggers comprises determining a number of the oneor more data assets affected by the one or more potential risk triggers.